└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # A low-level vision paperReading List for 2019 & 2020 2 | 3 | The following papers are related to low-level vision, including [denoise](#denoising), [inpainting](#inpainting), [lowlight enhancement](#lowlight-enhancement), [dehaze](#dehazing), [derain](#derain), [deblur](#deblur), [demoireing](#demoireing), [reflection removal](#reflection-removal), [super resolution](#super-resolution) and [image restoration](#image-restoration). 4 | 5 | Pull Requests are welcomed. Feel free to add related papers you like to this list. 6 | 7 | A lot of interesting papers are not yet covered by me in the arxiv sections. 8 | 9 | ## TODO 10 | 11 |
12 | Supported Conference List 13 | - [x] [IJCAI 2019](https://www.ijcai19.org/accepted-papers.html) 14 | - [x] [ICLR 2019](https://openreview.net/group?id=ICLR.cc/2019/Conference) 15 | - [x] [ICML 2019](https://icml.cc/Conferences/2019/Schedule?type=Poster) 16 | - [x] [CVPR 2019](http://openaccess.thecvf.com/CVPR2019_search.py) 17 | - [x] [AAAI 2019](https://aaai.org/Conferences/AAAI-19/wp-content/uploads/2018/11/AAAI-19_Accepted_Papers.pdf) 18 | - [x] [BMVC 2019](https://bmvc2019.org/programme/detailed-programme/#/) 19 | - [x] [ACM MM 2019](https://2019.acmmm.org/accepted-papers/index.html) 20 | - [x] [ICIP 2019](https://cmsworkshops.com/ICIP2019/Papers/TechnicalProgram_MS.asp) 21 | - [x] [ICME 2019](https://www.icme2019.org/conf_schedule) 22 | - [x] [ICCV 2019](http://openaccess.thecvf.com/ICCV2019.py) 23 | - [ ] NeurIPS 2019 24 | - [x] [AAAI 2020](https://aaai.org/Conferences/AAAI-20/wp-content/uploads/2020/01/AAAI-20-Accepted-Paper-List.pdf) 25 | - [x] [CVPR 2020](http://openaccess.thecvf.com/CVPR2020.py) 26 | - [x] [ECCV 2020](https://www.ecva.net/papers.php) 27 | - [x] [ACM MM 2020](https://2020.acmmm.org/main-track-list.html) 28 | 29 |
30 | 31 | `[Note]`: 32 | 33 | - some IJCAI accepted papers are not available at the moment 34 | - arxiv sections are updated on 2019.7.2 35 | - only titles are maintained for new papers 36 | 37 | ## Workshops 38 | 39 | - [NTIRE 2018](http://openaccess.thecvf.com/CVPR2018_workshops/CVPR2018_W13.py) 40 | - [NTIRE 2019](http://openaccess.thecvf.com/CVPR2019_workshops/CVPR2019_NTIRE.py) 41 | - [AIM 2019](http://openaccess.thecvf.com/ICCV2019_workshops/ICCV2019_AIM.py) 42 | - [UG2+ 2019](http://openaccess.thecvf.com/CVPR2019_workshops/CVPR2019_UG2_Prize_Challenge.py) 43 | - [Vision for All Seasons Bad Weather and Nighttime 2019](http://openaccess.thecvf.com/CVPR2019_workshops/CVPR2019_Vision_for_All_Seasons_Bad_Weather_and_Nighttime.py) 44 | 45 | ## Denoising 46 | 47 | (keywords: denoise, noise, denoising) 48 | 49 | ### CVPR19 50 | 51 | - Toward Convolutional Blind Denoising of Real Photographs 52 | - Shi Guo, Zifei Yan, Kai Zhang, Wangmeng Zuo, Lei Zhang 53 | - Noise2Void - Learning Denoising From Single Noisy Images 54 | - Alexander Krull, Tim-Oliver Buchholz, Florian Jug 55 | - FOCNet: A Fractional Optimal Control Network for Image Denoising 56 | - Xixi Jia, Sanyang Liu, Xiangchu Feng, Lei Zhang 57 | - Unprocessing Images for Learned Raw Denoising 58 | - Tim Brooks, Ben Mildenhall, Tianfan Xue, Jiawen Chen, Dillon Sharlet, Jonathan T. Barron 59 | - Model-Blind Video Denoising via Frame-To-Frame Training 60 | - Thibaud Ehret, Axel Davy, Jean-Michel Morel, Gabriele Facciolo, Pablo Arias 61 | 62 | ### ICML19 63 | 64 | - Noise2Self: Blind Denoising by Self-Supervision 65 | - Joshua Batson, Loic Royer 66 | 67 | ### ICCV19 68 | 69 | - Fully Convolutional Pixel Adaptive Image Denoiser 70 | - Sungmin Cha, Taesup Moon 71 | - CIIDefence: Defeating Adversarial Attacks by Fusing Class-Specific Image Inpainting and Image Denoising 72 | - Puneet Gupta, Esa Rahtu 73 | - Joint Demosaicking and Denoising by Fine-Tuning of Bursts of Raw Images 74 | - Thibaud Ehret, Axel Davy, Pablo Arias, Gabriele Facciolo 75 | - Self-Supervised Deep Depth Denoising 76 | - Vladimiros Sterzentsenko, Leonidas Saroglou, Anargyros Chatzitofis, Spyridon Thermos, Nikolaos Zioulis, Alexandros Doumanoglou, Dimitrios Zarpalas, Petros Daras 77 | - Self-Guided Network for Fast Image Denoising 78 | - Shuhang Gu, Yawei Li, Luc Van Gool, Radu Timofte 79 | - Real Image Denoising With Feature Attention 80 | - Saeed Anwar, Nick Barnes 81 | - NOTE-RCNN: NOise Tolerant Ensemble RCNN for Semi-Supervised Object Detection 82 | - Jiyang Gao, Jiang Wang, Shengyang Dai, Li-Jia Li, Ram Nevatia 83 | - Noise Flow: Noise Modeling With Conditional Normalizing Flows 84 | - Abdelrahman Abdelhamed, Marcus A. Brubaker, Michael S. Brown 85 | - Enhancing Low Light Videos by Exploring High Sensitivity Camera Noise 86 | - Wei Wang, Xin Chen, Cheng Yang, Xiang Li, Xuemei Hu, Tao Yue 87 | 88 | 89 | ### ICIP19 90 | 91 | - Enhancing denoised image via fusion with a noisy image 92 | - FULL-REFERENCE METRIC ADAPTIVE IMAGE DENOISING 93 | - IMAGE DENOISING WITH GRAPH-CONVOLUTIONAL NEURAL NETWORKS 94 | - ADAPTIVELY TUNING A CONVOLUTIONAL NEURAL NETWORK BY GATING PROCESS FOR IMAGE DENOISING 95 | - DVDnet: A Fast Network for Deep Video Denoising 96 | - MUTUAL NOISE ESTIMATION ALGORITHM FOR VIDEO DENOISING 97 | - COLOR IMAGE DENOISING USING QUATERNION ADAPTIVE NON-LOCAL COUPLED MEANS 98 | - MULTI-KERNEL PREDICTION NETWORKS FOR DENOISING OF BURST IMAGES 99 | - Simultaneous Nonlocal Self-Similarity Prior for Image Denoising 100 | - EDCNN: A NOVEL NETWORK FOR IMAGE DENOISING 101 | - A NON-LOCAL CNN FOR VIDEO DENOISING 102 | - ACCELERATING REDUNDANT DCT FILTERING FOR DEBLURRING AND DENOISING 103 | - NONLOCALITY-REINFORCED CONVOLUTIONAL NEURAL NETWORKS FOR IMAGE DENOISING 104 | 105 | ### ICME19 106 | 107 | - RESIDUAL DILATED NETWORK WITH ATTENTION FOR IMAGE BLIND DENOISING 108 | 109 | ### AAAI20 110 | 111 | - When AWGN-based Denoiser Meets Real Noises 112 | 113 | ### CVPR20 114 | 115 | - Noisier2Noise: Learning to Denoise From Unpaired Noisy Data 116 | - Joint Filtering of Intensity Images and Neuromorphic Events for High-Resolution Noise-Robust Imaging 117 | - A Physics-Based Noise Formation Model for Extreme Low-Light Raw Denoising 118 | - Transfer Learning From Synthetic to Real-Noise Denoising With Adaptive Instance Normalization 119 | - Variational-EM-Based Deep Learning for Noise-Blind Image Deblurring 120 | 121 | ### ECCV20 122 | 123 | - Reconstructing the Noise Variance Manifold for Image Denoising 124 | - Dual Adversarial Network: Toward Real-world Noise Removal and Noise Generation 125 | - Learning Noise-Aware Encoder-Decoder from Noisy Labels by Alternating Back-Propagation for Saliency Detection 126 | - Learning Camera-Aware Noise Models 127 | - Burst Denoising via Temporally Shifted Wavelet Transforms 128 | - Unpaired Learning of Deep Image Denoising 129 | - Practical Deep Raw Image Denoising on Mobile Devices 130 | - Stochastic Frequency Masking to Improve Super-Resolution and Denoising Networks 131 | - Spatial Hierarchy Aware Residual Pyramid Network for Time-of-Flight Depth Denoising 132 | - A Decoupled Learning Scheme for Real-world Burst Denoising from Raw Images 133 | - Robust and On-the-fly Dataset Denoising for Image Classification 134 | - Spatial-Adaptive Network for Single Image Denoising 135 | 136 | ### ACM MM20 137 | 138 | - Temporal Denoising Mask Synthesis Network for Learning Blind Video Temporal Consistency 139 | - Differentiable Manifold Reconstruction for Point Cloud Denoising 140 | 141 | ### arxiv 142 | 143 | - NLH: A Blind Pixel-level Non-local Method for Real-world Image Denoising 144 | - Yingkun Hou, Jun Xu, Mingxia Liu, Guanghai Liu, Li Liu, Fan Zhu, Ling Shao 145 | - Unsupervised Image Noise Modeling with Self-Consistent GAN 146 | - Hanshu Yan, Vincent Tan, Wenhan Yang, Jiashi Feng 147 | - Robust and interpretable blind image denoising via bias-free convolutional neural networks 148 | - Sreyas Mohan, Zahra Kadkhodaie, Eero P. Simoncelli, Carlos Fernandez-Granda 149 | - Learning Deep Image Priors for Blind Image Denoising 150 | - Xianxu Hou, Hongming Luo, Jingxin Liu, Bolei Xu, Ke Sun, Yuanhao Gong, Bozhi Liu, Guoping Qiu 151 | - Image Denoising with Graph-Convolutional Neural Networks 152 | - Diego Valsesia, Giulia Fracastoro, Enrico Magli 153 | - GAN2GAN: Generative Noise Learning for Blind Image Denoising with Single Noisy Images 154 | - Sungmin Cha, Taeeon Park, Taesup Moon 155 | - Segmentation-Aware Image Denoising without Knowing True Segmentation 156 | - Sicheng Wang, Bihan Wen, Junru Wu, Dacheng Tao, Zhangyang Wang 157 | - Joint demosaicing and denoising by overfitting of bursts of raw images 158 | - Thibaud Ehret, Axel Davy, Pablo Arias, Gabriele Facciolo 159 | - Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving Augmentation 160 | - Jiaming Liu, Chi-Hao Wu, Yuzhi Wang, Qin Xu, Yuqian Zhou, Haibin Huang, Chuan Wang, Shaofan Cai, Yifan Ding, Haoqiang Fan, Jue Wang 161 | - Efficient Blind Deblurring under High Noise Levels 162 | - Jérémy Anger, Mauricio Delbracio, Gabriele Facciolo 163 | - Generating Training Data for Denoising Real RGB Images via Camera Pipeline Simulation 164 | - Ronnachai Jaroensri Camille Biscarrat Miika Aittala Fredo Durand 165 | - Real Image Denoising with Feature Attention 166 | - Saeed Anwar, Nick Barnes 167 | - Learning Deformable Kernels for Image and Video Denoising 168 | - Xiangyu Xu, Muchen Li, Wenxiu Sun 169 | 170 | 171 | ## Inpainting 172 | 173 | (keywords: inpainting, completion) 174 | 175 | ### CVPR19 176 | 177 | - Learning Pyramid-Context Encoder Network for High-Quality Image Inpainting 178 | - Yanhong Zeng, Jianlong Fu, Hongyang Chao, Baining Guo 179 | - Deep Flow-Guided Video Inpainting 180 | - Rui Xu, Xiaoxiao Li, Bolei Zhou, Chen Change Loy 181 | - Deep Video Inpainting 182 | - Dahun Kim, Sanghyun Woo, Joon-Young Lee, In So Kweon 183 | - Foreground-Aware Image Inpainting 184 | - Wei Xiong, Jiahui Yu, Zhe Lin, Jimei Yang, Xin Lu, Connelly Barnes, Jiebo Luo 185 | - PEPSI : Fast Image Inpainting With Parallel Decoding Network 186 | - Min-cheol Sagong, Yong-goo Shin, Seung-wook Kim, Seung Park, Sung-jea Ko 187 | - Coordinate-Based Texture Inpainting for Pose-Guided Human Image Generation 188 | - Artur Grigorev, Artem Sevastopolsky, Alexander Vakhitov, Victor Lempitsky 189 | 190 | ### IJCAI19 191 | 192 | - Coarse-to-Fine Image Inpainting via Region-wise Convolutions and Non-Local Correlation 193 | - Yuqing Ma, Xianglong Liu, Shihao Bai, Lei Wang, Dailan He, Aishan Liu 194 | - Generative Image Inpainting with Submanifold Alignment 195 | - Ang Li, Jianzhong Qi, Rui Zhang, Xingjun Ma, Ramamohanarao Kotagiri 196 | - MUSICAL: Multi-Scale Image Contextual Attention Learning for Inpainting 197 | - Ning Wang, Jingyuan Li, Lefei Zhang, Bo Du 198 | 199 | ### AAAI19 200 | 201 | - CISI-net: Explicit latent content inference and imitated style rendering for image inpainting 202 | - Jing Xiao, liang liao, Qiegen Liu, Ruimin Hu 203 | - Video Inpainting by Jointly Learning Temporal Structure and Spatial Details 204 | - Chuan Wang, Haibin Huang, Xiaoguang Han, Jue Wang 205 | 206 | ### ICCV19 207 | 208 | - Coherent Semantic Attention for Image Inpainting 209 | - Hongyu Liu, Bin Jiang, Yi Xiao, Chao Yang 210 | - StructureFlow: Image Inpainting via Structure-Aware Appearance Flow 211 | - Yurui Ren, Xiaoming Yu, Ruonan Zhang, Thomas H. Li, Shan Liu, Ge Li 212 | - Vision-Infused Deep Audio Inpainting 213 | - Hang Zhou, Ziwei Liu, Xudong Xu, Ping Luo, Xiaogang Wang 214 | - An Internal Learning Approach to Video Inpainting 215 | - Haotian Zhang, Long Mai, Ning Xu, Zhaowen Wang, John Collomosse, Hailin Jin 216 | - Copy-and-Paste Networks for Deep Video Inpainting 217 | - Sungho Lee, Seoung Wug Oh, DaeYeun Won, Seon Joo Kim 218 | - Free-Form Image Inpainting With Gated Convolution 219 | - Jiahui Yu, Zhe Lin, Jimei Yang, Xiaohui Shen, Xin Lu, Thomas S. Huang 220 | - FiNet: Compatible and Diverse Fashion Image Inpainting 221 | - Xintong Han, Zuxuan Wu, Weilin Huang, Matthew R. Scott, Larry S. Davis 222 | - Progressive Reconstruction of Visual Structure for Image Inpainting 223 | - Jingyuan Li, Fengxiang He, Lefei Zhang, Bo Du, Dacheng Tao 224 | - Human Motion Prediction via Spatio-Temporal Inpainting 225 | - Alejandro Hernandez, Jurgen Gall, Francesc Moreno-Noguer 226 | - Localization of Deep Inpainting Using High-Pass Fully Convolutional Network 227 | - Haodong Li, Jiwu Huang 228 | - Image Inpainting With Learnable Bidirectional Attention Maps 229 | - Chaohao Xie, Shaohui Liu, Chao Li, Ming-Ming Cheng, Wangmeng Zuo, Xiao Liu, Shilei Wen, Errui Ding 230 | - Free-Form Video Inpainting With 3D Gated Convolution and Temporal PatchGAN 231 | - Ya-Liang Chang, Zhe Yu Liu, Kuan-Ying Lee, Winston Hsu 232 | 233 | ### BMVC19 234 | 235 | - Base-Detail Image Inpainting 236 | - Learnable Gated Temporal Shift Module for Free-form Video Inpainting 237 | 238 | ### ACM MM19 239 | 240 | - Progressive Image Inpainting with Full-Resolution Residual Network 241 | - GAIN: Gradient Augmented Inpainting Network for Irregular Holes 242 | - Single-shot Semantic Image Inpainting with Densely Connected Generative 243 | - Deep Fusion Network for Image Completion 244 | 245 | ### ICIP19 246 | 247 | - FASTER UNSUPERVISED SEMANTIC INPAINTING: A GAN BASED APPROACH 248 | - IMAGE INPAINTING FOR RANDOM AREAS USING DENSE CONTEXT FEATURES 249 | - WEIGHTED SCHATTEN P-NORM MINIMIZATION WITH LOCAL AND NONLOCAL CONSTRAINTS FOR NOISY IMAGE COMPLETION 250 | 251 | ### AAAI20 252 | 253 | - Learning to Incorporate Structure Knowledge for Image Inpainting 254 | 255 | ### CVPR20 256 | 257 | - Prior Guided GAN Based Semantic Inpainting 258 | - UCTGAN: Diverse Image Inpainting Based on Unsupervised Cross-Space Translation 259 | - Contextual Residual Aggregation for Ultra High-Resolution Image Inpainting 260 | - Recurrent Feature Reasoning for Image Inpainting 261 | - 3D Photography Using Context-Aware Layered Depth Inpainting 262 | 263 | ### ECCV20 264 | 265 | - Rethinking Image Inpainting via a Mutual Encoder-Decoder with Feature Equalizations 266 | - Short-Term and Long-Term Context Aggregation Network for Video Inpainting 267 | - Learning Object Placement by Inpainting for Compositional Data Augmentation 268 | - Learning Joint Spatial-Temporal Transformations for Video Inpainting 269 | - High-Resolution Image Inpainting with Iterative Confidence Feedback and Guided Upsampling 270 | - DVI: Depth Guided Video Inpainting for Autonomous Driving 271 | - VCNet: A Robust Approach to Blind Image Inpainting 272 | - Guidance and Evaluation: Semantic-Aware Image Inpainting for Mixed Scenes 273 | - Proposal-based Video Completion 274 | - Synthesis and Completion of Facades from Satellite Imagery 275 | - SoftPoolNet: Shape Descriptor for Point Cloud Completion and Classification 276 | - Multimodal Shape Completion via Conditional Generative Adversarial Networks 277 | - Weakly-supervised 3D Shape Completion in the Wild 278 | - GRNet: Gridding Residual Network for Dense Point Cloud Completion 279 | - Flow-edge Guided Video Completion 280 | - Non-Local Spatial Propagation Network for Depth Completion 281 | - Sparse-to-Dense Depth Completion Revisited: Sampling Strategy and Graph Construction 282 | - Towards Precise Completion of Deformable Shapes 283 | - Detail Preserved Point Cloud Completion via Separated Feature Aggregation 284 | 285 | ### ACM MM 20 286 | 287 | - Image Inpainting Based on Multi-frequency Probabilistic Inference Model 288 | - MMFL: Multimodal Fusion Learning for Text-Guided Image Inpainting 289 | - Text-Guided Neural Image Inpainting 290 | 291 | ### arxiv 292 | 293 | - Coherent Semantic Attention for Image Inpainting 294 | - Hongyu Liu, Bin Jiang, Yi Xiao, Chao Yang 295 | - PEPSI++: Fast and Lightweight Network for Image Inpainting 296 | - Yong-Goo Shin, Min-Cheol Sagong, Yoon-Jae Yeo, Seung-Wook Kim, Sung-Jea Ko 297 | - Deep Flow-Guided Video Inpainting 298 | - Rui Xu, Xiaoxiao Li, Bolei Zhou, Chen Change Loy 299 | - Learning Pyramid-Context Encoder Network for High-Quality Image Inpainting 300 | - Yanhong Zeng, Jianlong Fu, Hongyang Chao, Baining Guo 301 | - Compatible and Diverse Fashion Image Inpainting 302 | - Xintong Han, Zuxuan Wu, Weilin Huang, Matthew R. Scott, Larry S. Davis 303 | - Deep Hyperspectral Prior: Denoising, Inpainting, Super-Resolution 304 | - Oleksii Sidorov, Jon Yngve Hardeberg 305 | - EdgeConnect: Generative Image Inpainting with Adversarial Edge Learning 306 | - Kamyar Nazeri, Eric Ng, Tony Joseph, Faisal Z. Qureshi, Mehran Ebrahimi 307 | 308 | ## Lowlight Enhancement 309 | 310 | (keywords: lowlight, underexposed, illumination, contrast enhancement, low light, dark, HDR) 311 | 312 | ### CVPR19 313 | 314 | - Underexposed Photo Enhancement Using Deep Illumination Estimation 315 | - Ruixing Wang, Qing Zhang, Chi-Wing Fu, Xiaoyong Shen, Wei-Shi Zheng, Jiaya Jia 316 | 317 | ### ICCV19 318 | 319 | - A Dataset of Multi-Illumination Images in the Wild 320 | - Lukas Murmann, Michael Gharbi, Miika Aittala, Fredo Durand 321 | - No Fear of the Dark: Image Retrieval Under Varying Illumination Conditions 322 | - Tomas Jenicek, Ondrej Chum 323 | - Enhancing Low Light Videos by Exploring High Sensitivity Camera Noise 324 | - Wei Wang, Xin Chen, Cheng Yang, Xiang Li, Xuemei Hu, Tao Yue 325 | - Seeing Motion in the Dark 326 | - Chen Chen, Qifeng Chen, Minh N. Do, Vladlen Koltun 327 | - Learning to See Moving Objects in the Dark 328 | - Haiyang Jiang, Yinqiang Zheng 329 | 330 | 331 | ### BMVC19 332 | 333 | - BIRD: Learning Binary and Illumination Robust Descriptor for Face Recognition 334 | 335 | ### ACM MM19 336 | 337 | - Progressive Retinex: Mutually Reinforced Illumination-Noise Perception Network for Low-Light Image Enhancement 338 | - Adaptive Feature Fusion via Graph Neural Network for Person Re-identification Illumination-Invariant Person Re-Identification 339 | - Kindling the Darkness: A Practical Low-light Image Enhancer 340 | 341 | ### ICIP19 342 | 343 | - WHAT'S THERE IN THE DARK 344 | - LLRNET: A MULTISCALE SUBBAND LEARNING APPROACH FOR LOW LIGHT IMAGE RESTORATION 345 | 346 | ### CVPR20 347 | 348 | - Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement 349 | - Learning to Restore Low-Light Images via Decomposition-and-Enhancement 350 | - From Fidelity to Perceptual Quality: A Semi-Supervised Approach for Low-Light Image Enhancement 351 | 352 | ### ECCV20 353 | 354 | - Learning Flow-based Feature Warping for Face Frontalization with Illumination Inconsistent Supervision 355 | - Object-based Illumination Estimation with Rendering-aware Neural Networks 356 | - Low Light Video Enhancement using Synthetic Data Produced with an Intermediate Domain Mapping 357 | - Image Classification in the Dark using Quanta Image Sensors 358 | - Learning to See in the Dark with Events 359 | - YOLO in the Dark - Domain Adaptation Method for Merging Multiple Models - 360 | - Towards Practical and Efficient High-Resolution HDR Deghosting with CNN 361 | 362 | ### ACM MM20 363 | 364 | - Fast Enhancement for Non-Uniform Illumination Images using Light-weight CNNs 365 | - NuI-Go: Recursive Non-local Encoder-Decoder Network for Retinal Image Non-uniform Illumination Removal 366 | 367 | ### arxiv 368 | 369 | - [Kindling the Darkness: A Practical Low-light Image Enhancer](https://arxiv.org/pdf/1905.04161v1.pdf) 370 | - Yonghua Zhang, Jiawan Zhang, and Xiaojie Guo 371 | - [EnlightenGAN: Deep Light Enhancement without Paired Supervision](https://arxiv.org/pdf/1906.06972.pdf) 372 | - Yifan Jiang, Xinyu Gong, Ding Liu, Yu Cheng, Chen Fang, Xiaohui Shen, Jianchao Yang, Pan Zhou, Zhangyang Wang 373 | - [Low-light Image Enhancement Algorithm Based on Retinex and Generative Adversarial Network](https://arxiv.org/abs/1906.06027) 374 | - Yangming Shi, Xiaopo Wu, Ming Zhu 375 | - [LED2Net: Deep Illumination-aware Dehazing with Low-light and Detail Enhancement](https://arxiv.org/abs/1906.05119) 376 | - Guisik Kim, Junseok Kwon 377 | - [A Noise-aware Enhancement Method for Underexposed Images](https://arxiv.org/abs/1904.10961) 378 | - Chien-Cheng Chien, Yuma Kinoshita, Hitoshi Kiya 379 | - [End-to-End Denoising of Dark Burst Images Using Recurrent Fully Convolutional Networks](https://arxiv.org/abs/1904.07483) 380 | - Di Zhao, Lan Ma, Songnan Li, Dahai Yu 381 | - [A Retinex-based Image Enhancement Scheme with Noise Aware Shadow-up Function](https://arxiv.org/abs/1811.03280) 382 | - Chien Cheng Chien, Yuma Kinoshita, Sayaka Shiota, Hitoshi Kiya 383 | 384 | ## Dehazing 385 | 386 | (keywords: dehazing, haze) 387 | 388 | ### CVPR19 389 | 390 | - Enhanced Pix2pix Dehazing Network 391 | - Yanyun Qu, Yizi Chen, Jingying Huang, Yuan Xie 392 | - PMS-Net: Robust Haze Removal Based on Patch Map for Single Images 393 | - Wei-Ting Chen, Jian-Jiun Ding, Sy-Yen Kuo 394 | 395 | ### IJCAI19 396 | 397 | - Dual-Path in Dual-Path Network for Single Image Dehazing 398 | - Aiping Yang, Haixin Wang, Zhong Ji, Yanwei Pang, Ling Shao 399 | 400 | ### ICCV19 401 | - Deep Multi-Model Fusion for Single-Image Dehazing 402 | - Zijun Deng, Lei Zhu, Xiaowei Hu, Chi-Wing Fu, Xuemiao Xu, Qing Zhang, Jing Qin, Pheng-Ann Heng 403 | - Learning Deep Priors for Image Dehazing 404 | - Yang Liu, Jinshan Pan, Jimmy Ren, Zhixun Su 405 | - LAP-Net: Level-Aware Progressive Network for Image Dehazing 406 | - Yunan Li, Qiguang Miao, Wanli Ouyang, Zhenxin Ma, Huijuan Fang, Chao Dong, Yining Quan 407 | - GridDehazeNet: Attention-Based Multi-Scale Network for Image Dehazing 408 | - Xiaohong Liu, Yongrui Ma, Zhihao Shi, Jun Chen 409 | 410 | ### ICIP19 411 | 412 | - WAVELET U-NET AND THE CHROMATIC ADAPTATION TRANSFORM FOR SINGLE IMAGE DEHAZING 413 | - Towards Unsupervised Single Image Dehazing with Deep Learning 414 | - Convolutional AutoEncoder for Single Image Dehazing 415 | - SEQUENTIALLY REFINED SPATIAL AND CHANNEL-WISE FEATURE AGGREGATION IN ENCODER-DECODER NETWORK FOR SINGLE IMAGE DEHAZING 416 | - DENSE-HAZE: A BENCHMARK FOR IMAGE DEHAZING WITH DENSE-HAZE AND HAZE-FREE IMAGES 417 | - VARIATIONAL REGULARIZED TRANSMISSION REFINEMENT FOR IMAGE DEHAZING 418 | - Feature Aggregation Convolution Network For Haze Removal 419 | 420 | ### AAAI20 421 | 422 | - FD-GAN: Generative Adversarial Networks with Fusion-discriminator for Single Image Dehazing 423 | - FFA-Net: Feature Fusion Attention Network for Single Image Dehazing 424 | 425 | ### CVPR20 426 | 427 | - Multi-Scale Boosted Dehazing Network With Dense Feature Fusion 428 | - Domain Adaptation for Image Dehazing 429 | - Distilling Image Dehazing With Heterogeneous Task Imitation 430 | - BidNet: Binocular Image Dehazing Without Explicit Disparity Estimation 431 | 432 | ### ECCV20 433 | 434 | - Physics-based Feature Dehazing Networks 435 | - HardGAN: A Haze-Aware Representation Distillation GAN for Single Image Dehazing 436 | 437 | ### ACM MM20 438 | 439 | - Discrete Haze Level Dehazing Network 440 | - Nighttime Dehazing with a Synthetic Benchmark 441 | 442 | ### arxiv 443 | 444 | - FAMED-Net: A Fast and Accurate Multi-scale End-to-end Dehazing Network 445 | - Jing Zhang, Dacheng Tao 446 | - Joint haze image synthesis and dehazing with mmd-vae losses 447 | - Zongliang Li, Chi Zhang, Gaofeng Meng, Yuehu Liu 448 | - Weighted Dark Channel Dehazing 449 | - Zhu Mingzhu, He Bingwei, Liu Jiantao 450 | - Multiple Linear Regression Haze-removal Model Based on Dark Channel Prior 451 | - Binghan Li, Wenrui Zhang, Mi Lu 452 | - Feature Forwarding for Efficient Single Image Dehazing 453 | - Peter Morales, Tzofi Klinghoffer, Seung Jae Lee 454 | - Fast Single Image Dehazing via Multilevel Wavelet Transform based Optimization 455 | - Jiaxi He, Frank Z. Xing, Ran Yang, Cishen Zhang 456 | - A Light Dual-Task Neural Network for Haze Removal 457 | - Yu Zhang, Xinchao Wang, Xiaojun Bi, Dacheng Tao 458 | - Dense Haze: A benchmark for image dehazing with dense-haze and haze-free images 459 | - Codruta O. Ancuti, Cosmin Ancuti, Mateu Sbert, Radu Timofte 460 | - Learning of Image Dehazing Models for Segmentation Tasks 461 | - Sébastien de Blois, Ihsen Hedhli, Christian Gagné 462 | - Variational Regularized Transmission Refinement for Image Dehazing 463 | - Qiaoling Shu, Chuansheng Wu, Zhe Xiao, Ryan Wen Liu 464 | - Night Time Haze and Glow Removal using Deep Dilated Convolutional Network 465 | - Shiba Kuanar, K.R. Rao, Dwarikanath Mahapatra, Monalisa Bilas 466 | - Unsupervised Single Image Dehazing Using Dark Channel Prior Loss 467 | - Alona Golts, Daniel Freedman, Michael Elad 468 | - Image Dehazing via Joint Estimation of Transmittance Map and Environmental Illumination 469 | - Sanchayan Santra, Ranjan Mondal, Pranoy Panda, Nishant Mohanty, Shubham Bhuyan 470 | - Reconstruction Loss Minimized FCN for Single Image Dehazing 471 | - Shirsendu Sukanta Halder, Sanchayan Santra, Bhabatosh Chanda 472 | - Single Image Haze Removal Using Conditional Wasserstein Generative Adversarial Networks 473 | - Joshua Peter Ebenezer, Bijaylaxmi Das, Sudipta Mukhopadhyay 474 | 475 | 476 | ## Derain 477 | 478 | (keywords: rain, deraining) 479 | 480 | ### CVPR19 481 | 482 | - Heavy Rain Image Restoration: Integrating Physics Model and Conditional Adversarial Learning 483 | - Ruoteng Li, Loong-Fah Cheong, Robby T. Tan 484 | - Semi-Supervised Transfer Learning for Image Rain Removal 485 | - Wei Wei, Deyu Meng, Qian Zhao, Zongben Xu, Ying Wu 486 | - Depth-Attentional Features for Single-Image Rain Removal 487 | - Xiaowei Hu, Chi-Wing Fu, Lei Zhu, Pheng-Ann Heng 488 | - Spatial Attentive Single-Image Deraining With a High Quality Real Rain Dataset 489 | - Tianyu Wang, Xin Yang, Ke Xu, Shaozhe Chen, Qiang Zhang, Rynson W.H. Lau 490 | - Frame-Consistent Recurrent Video Deraining With Dual-Level Flow 491 | - Wenhan Yang, Jiaying Liu, Jiashi Feng 492 | - Single Image Deraining: A Comprehensive Benchmark Analysis 493 | - Siyuan Li, Iago Breno Araujo, Wenqi Ren, Zhangyang Wang, Eric K. Tokuda, Roberto Hirata Junior, Roberto Cesar-Junior, Jiawan Zhang, Xiaojie Guo, Xiaochun Cao 494 | - Progressive Image Deraining Networks: A Better and Simpler Baseline 495 | - Dongwei Ren, Wangmeng Zuo, Qinghua Hu, Pengfei Zhu, Deyu Meng 496 | 497 | ### AAAI19 498 | 499 | - RR-GAN: Single Image Rain Removal Without Paired Information 500 | - Hongyuan Zh, Xi Peng, Joey Tianyi Zhou, Songfan Yang, Vijay Chandrasekhar, Liyuan Li, Joo-Hwee Lim 501 | 502 | ### ICCV19 503 | 504 | - RainFlow: Optical Flow Under Rain Streaks and Rain Veiling Effect 505 | - Ruoteng Li, Robby T. Tan, Loong-Fah Cheong, Angelica I. Aviles-Rivero, Qingnan Fan, Carola-Bibiane Schonlieb 506 | - Physics-Based Rendering for Improving Robustness to Rain 507 | - Shirsendu Sukanta Halder, Jean-Francois Lalonde, Raoul de Charette 508 | - ERL-Net: Entangled Representation Learning for Single Image De-Raining 509 | 510 | 511 | 512 | ### BMVC19 513 | 514 | - Residual Multiscale Based Single Image Deraining 515 | 516 | ### ACM MM19 517 | 518 | - Single Image Deraining via Recurrent Hierarchy Enhancement Network 519 | - Gradual Network for Single Image De-raining 520 | - DTDN: Dual-task De-raining Network 521 | 522 | ### ICIP19 523 | 524 | - DUAL RECURSIVE NETWORK FOR FAST IMAGE DERAINING 525 | - OND SYNTHETIC DATA: A BLIND DERAINING QUALITY ASSESSMENT METRIC TOWARDS AUTHENTIC RAIN IMAGE 526 | - Denoising Adversarial Networks for Rain Removal and Reflection Removal 527 | - RAIN STREAKS REMOVAL FOR SINGLE IMAGE VIA DIRECTIONAL TOTAL VARIATION REGULARIZATION 528 | - Single-image rain removal via multi-scale cascading image generation 529 | - UNSUPERVISED SINGLE IMAGE DERAINING WITH SELF-SUPERVISED CONSTRAINTS 530 | - SELF-REFINING DEEP SYMMETRY ENHANCED NETWORK FOR RAIN REMOVAL 531 | - DUAL-DOMAIN SINGLE IMAGE DE-RAINING USING CONDITIONAL GENERATIVE ADVERSARIAL NETWORK 532 | 533 | ### ICME19 534 | 535 | - REMOVING RAIN IN VIDEOS: A LARGE-SCALE DATABASE AND A TWO-STREAM CONVLSTM AP- PROACH 536 | 537 | ### AAAI20 538 | 539 | - Towards Scale-Free Rain Streak Removal via Self-Supervised Fractal Band Learning 540 | 541 | ### CVPR20 542 | 543 | - Syn2Real Transfer Learning for Image Deraining Using Gaussian Processes 544 | - Multi-Scale Progressive Fusion Network for Single Image Deraining 545 | - Detail-recovery Image Deraining via Context Aggregation Networks 546 | 547 | ### ECCV20 548 | 549 | - Rethinking Image Deraining via Rain Streaks and Vapors 550 | - Beyond Monocular Deraining: Stereo Image Deraining via Semantic Understanding 551 | - Rethinking Image Deraining via Rain Streaks and Vapors 552 | 553 | ### ACM MM20 554 | 555 | - Single Image Deraining via Scale-space Invariant Attention Neural Network 556 | - Joint Self-Attention and Scale-Aggregation for Self-Calibrated Deraining Network 557 | - Deep Cross-scale Fusion Network for Single Image Rain Removal 558 | 559 | ### arxiv 560 | 561 | - Morphological Networks for Image De-raining 562 | - Ranjan Mondal, Pulak Purkait, Sanchayan Santra, Bhabatosh Chanda 563 | - A Deep Tree-Structured Fusion Model for Single Image Deraining 564 | - Xueyang Fu, Qi Qi, Yue Huang, Xinghao Ding, Feng Wu, John Paisley 565 | - Unsupervised Single Image Deraining with Self-supervised Constraints 566 | - Xin Jin, Zhibo Chen, Jianxin Lin, Zhikai Chen, Wei Zhou 567 | - Deep Single Image Deraining Via Estimating Transmission and Atmospheric Light in rainy Scenes 568 | - Yinglong Wang, Qinfeng Shi, Ehsan Abbasnejad, Chao Ma, Xiaoping Ma, Bing Zeng 569 | - An Effective Two-Branch Model-Based Deep Network for Single Image Deraining 570 | - Yinglong Wang, Dong Gong, Jie Yang, Qinfeng Shi, Anton van den Hengel, Dehua Xie, Bing Zeng 571 | - Rain O'er Me: Synthesizing real rain to derain with data distillation 572 | - Huangxing Lin, Yanlong Li, Xinghao Ding, Weihong Zeng, Yue Huang, John Paisley 573 | - Directional Regularized Tensor Modeling for Video Rain Streaks Removal 574 | - Zhaoyang Sun, Shengwu Xiong, Ryan Wen Liu 575 | - Gated Context Aggregation Network for Image Dehazing and Deraining 576 | - Dongdong Chen, Mingming He, Qingnan Fan, Jing Liao, Liheng Zhang, Dongdong Hou, Lu Yuan, Gang Hua 577 | 578 | ## Deblur 579 | 580 | ### CVPR19 581 | 582 | - Blind Image Deblurring With Local Maximum Gradient Prior 583 | - Liang Chen, Faming Fang, Tingting Wang, Guixu Zhang 584 | - Dynamic Scene Deblurring With Parameter Selective Sharing and Nested Skip Connections 585 | - Hongyun Gao, Xin Tao, Xiaoyong Shen, Jiaya Jia 586 | - Deep Stacked Hierarchical Multi-Patch Network for Image Deblurring 587 | - Hongguang Zhang, Yuchao Dai, Hongdong Li, Piotr Koniusz 588 | - Phase-Only Image Based Kernel Estimation for Single Image Blind Deblurring 589 | - Liyuan Pan, Richard Hartley, Miaomiao Liu, Yuchao Dai 590 | - Recurrent Neural Networks With Intra-Frame Iterations for Video Deblurring 591 | - Seungjun Nah, Sanghyun Son, Kyoung Mu Lee 592 | 593 | ### ICCV19 594 | 595 | - FAB: A Robust Facial Landmark Detection Framework for Motion-Blurred Videos 596 | - Keqiang Sun, Wayne Wu, Tinghao Liu, Shuo Yang, Quan Wang, Qiang Zhou, Zuochang Ye, Chen Qian 597 | - Human-Aware Motion Deblurring 598 | - Ziyi Shen, Wenguan Wang, Xiankai Lu, Jianbing Shen, Haibin Ling, Tingfa Xu, Ling Shao 599 | - Unconstrained Motion Deblurring for Dual-Lens Cameras 600 | - M. R. Mahesh Mohan, Sharath Girish, A. N. Rajagopalan 601 | 602 | ### ACM MM19 603 | 604 | - Tell Me Where It is Still Blurry: Adversarial Blurred Region Mining and Refining 605 | 606 | ### ICIP19 607 | 608 | - ADVERSARIAL REPRESENTATION LEARNING FOR DYNAMIC SCENE DEBLURRING: A SIMPLE, FAST AND ROBUST APPROACH 609 | - UPDCNN: A NEW SCHEME FOR IMAGE UPSAMPLING AND DEBLURRING USING A DEEP CONVOLUTIONAL NEURAL NETWORK 610 | - LEARNED IMAGE DEBLURRING BY UNFOLDING A PROXIMAL INTERIOR POINT ALGORITHM 611 | - EFFICIENT MOTION DEBLURRING WITH FEATURE TRANSFORMATION AND SPATIAL ATTENTION 612 | - Joint Demosaicking and Blind Deblurring Using Deep Convolutional Neural Network 613 | - ACCELERATING REDUNDANT DCT FILTERING FOR DEBLURRING AND DENOISING 614 | 615 | ### ICME19 616 | 617 | - JOINTLY SOLVING DEBLURRING AND SUPER-RESOLUTION PROBLEMS WITH DUAL SUPER- VISED NETWORK 618 | - SCALE-AWARE DEEP NETWORK WITH HOLE CONVOLUTION FOR BLIND MOTION DEBLURRING 619 | 620 | ### AAAI20 621 | 622 | - Learning to Deblur Face Images via Sketch Synthesis 623 | 624 | ### CVPR20 625 | 626 | - Deblurring by Realistic Blurring 627 | - Cascaded Deep Video Deblurring Using Temporal Sharpness Prior 628 | - Learning Event-Based Motion Deblurring 629 | - Efficient Dynamic Scene Deblurring Using Spatially Variant Deconvolution Network With Optical Flow Guided Training 630 | - Spatially-Attentive Patch-Hierarchical Network for Adaptive Motion Deblurring 631 | - Variational-EM-Based Deep Learning for Noise-Blind Image Deblurring 632 | - Deblurring Using Analysis-Synthesis Networks Pair 633 | 634 | 635 | ### ECCV20 636 | 637 | - Efficient Spatio-Temporal Recurrent Neural Network for Video Deblurring 638 | - Multi-Temporal Recurrent Neural Networks For Progressive Non-Uniform Single Image Deblurring With Incremental Temporal Training 639 | - Learning Event-Driven Video Deblurring and Interpolation 640 | - Defocus Deblurring Using Dual-Pixel Data 641 | - Rethinking the Defocus Blur Detection Problem and A Real-Time Deep DBD Model 642 | - Defocus Blur Detection via Depth Distillation 643 | - End-to-end Interpretable Learning of Non-blind Image Deblurring 644 | - Real-World Blur Dataset for Learning and Benchmarking Deblurring Algorithms 645 | - OID: Outlier Identifying and Discarding in Blind Image Deblurring 646 | - Enhanced Sparse Model for Blind Deblurring 647 | 648 | ### ACM MM20 649 | 650 | - ALANET: Adaptive Latent Attention Network for Joint Video Deblurring and Interpolation 651 | 652 | ### arxiv 653 | 654 | - Blind Image Deblurring Using Patch-Wise Minimal Pixels Regularization 655 | - Fei Wen, Rendong Ying, Peilin Liu, Trieu-Kien Truong 656 | - Single Image Blind Deblurring Using Multi-Scale Latent Structure Prior 657 | - Yuanchao Bai, Huizhu Jia, Ming Jiang, Xianming Liu, Xiaodong Xie, Wen Gao 658 | - Efficient Blind Deblurring under High Noise Levels 659 | - Jérémy Anger, Mauricio Delbracio, Gabriele Facciolo 660 | - Coupled Learning for Facial Deblur 661 | - Dayong Tian, Dacheng Tao 662 | - Deep Stacked Hierarchical Multi-patch Network for Image Deblurring 663 | - Hongguang Zhang, Yuchao Dai, Hongdong Li, Piotr Koniusz 664 | - Fast and Full-Resolution Light Field Deblurring using a Deep Neural Network 665 | - Jonathan Samuel Lumentut, Tae Hyun Kim, Ravi Ramamoorthi, In Kyu Park 666 | - Kernel-Free Image Deblurring with a Pair of Blurred/Noisy Images 667 | - Chunzhi Gu, Xuequan Lu, Ying He, Chao Zhang 668 | - Down-Scaling with Learned Kernels in Multi-Scale Deep Neural Networks for Non-Uniform Single Image Deblurring 669 | - Dongwon Park, Jisoo Kim, Se Young Chun 670 | - Single Image Deblurring and Camera Motion Estimation with Depth Map 671 | - Liyuan Pan, Yuchao Dai, Miaomiao Liu 672 | - An Algorithm Unrolling Approach to Deep Image Deblurring 673 | - Yuelong Li, Mohammad Tofighi, Vishal Monga, Yonina C. Eldar 674 | - Deep Algorithm Unrolling for Blind Image Deblurring 675 | - Yuelong Li, Mohammad Tofighi, Junyi Geng, Vishal Monga, Yonina C. Eldar 676 | 677 | ## Demoireing 678 | 679 | (keywords: demoireing, moire, Moiré) 680 | 681 | ### ICCV19 682 | 683 | - Mop Moire Patterns Using MopNet 684 | 685 | ### CVPR20 686 | 687 | - Image Demoireing with Learnable Bandpass Filters 688 | 689 | ### ECCV20 690 | 691 | - Wavelet-Based Dual-Branch Network for Image Demoiréing 692 | - FHDe²Net: Full High Definition Demoireing Network 693 | 694 | ## Reflection removal 695 | 696 | (keywords: reflection, deglare) 697 | 698 | ### CVPR19 699 | 700 | - Reflection Removal Using a Dual-Pixel Sensor 701 | - Single Image Reflection Removal Beyond Linearity 702 | - Fast Single Image Reflection Suppression via Convex Optimization 703 | - Single Image Reflection Removal Exploiting Misaligned Training Data and Network Enhancements 704 | 705 | ### ICCV19 706 | 707 | - Learning to jointly generate and separate reflections 708 | 709 | ### ICIP19 710 | 711 | - Denoising Adversarial Networks for Rain Removal and Reflection Removal 712 | - Multi-Modal Reflection Removal Using Convolutional Neural Networks 713 | 714 | ### NeurIPS19 715 | 716 | - Reflection Separation using a Pair of Unpolarized and Polarized Images 717 | 718 | ### CVPR20 719 | 720 | - Polarized Reflection Removal With Perfect Alignment in the Wild 721 | - Reflection Scene Separation From a Single Image 722 | - Single Image Reflection Removal Through Cascaded Refinement 723 | - Single Image Reflection Removal With Physically-Based Training Images 724 | 725 | ### ECCV20 726 | 727 | - Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networks 728 | - Reflection Separation via Multi-bounce Polarization State Tracing 729 | 730 | ## Super Resolution 731 | 732 | ### CVPR19 733 | 734 | - Deep Plug-And-Play Super-Resolution for Arbitrary Blur Kernels 735 | - Kai Zhang, Wangmeng Zuo, Lei Zhang 736 | - Meta-SR: A Magnification-Arbitrary Network for Super-Resolution 737 | - Xuecai Hu, Haoyuan Mu, Xiangyu Zhang, Zilei Wang, Tieniu Tan, Jian Sun 738 | - Blind Super-Resolution With Iterative Kernel Correction 739 | - Jinjin Gu, Hannan Lu, Wangmeng Zuo, Chao Dong 740 | - Camera Lens Super-Resolution 741 | - Chang Chen, Zhiwei Xiong, Xinmei Tian, Zheng-Jun Zha, Feng Wu 742 | - Towards Real Scene Super-Resolution With Raw Images 743 | - Xiangyu Xu, Yongrui Ma, Wenxiu Sun 744 | - ODE-Inspired Network Design for Single Image Super-Resolution 745 | - Xiangyu He, Zitao Mo, Peisong Wang, Yang Liu, Mingyuan Yang, Jian Cheng 746 | - Feedback Network for Image Super-Resolution 747 | - Zhen Li, Jinglei Yang, Zheng Liu, Xiaomin Yang, Gwanggil Jeon, Wei Wu 748 | - Recurrent Back-Projection Network for Video Super-Resolution 749 | - Muhammad Haris, Gregory Shakhnarovich, Norimichi Ukita 750 | - Image Super-Resolution by Neural Texture Transfer 751 | - Zhifei Zhang, Zhaowen Wang, Zhe Lin, Hairong Qi 752 | - Natural and Realistic Single Image Super-Resolution With Explicit Natural Manifold Discrimination 753 | - Jae Woong Soh, Gu Yong Park, Junho Jo, Nam Ik Cho 754 | - Fast Spatio-Temporal Residual Network for Video Super-Resolution 755 | - Sheng Li, Fengxiang He, Bo Du, Lefei Zhang, Yonghao Xu, Dacheng Tao 756 | - Residual Networks for Light Field Image Super-Resolution 757 | - Shuo Zhang, Youfang Lin, Hao Sheng 758 | - Second-Order Attention Network for Single Image Super-Resolution 759 | - Tao Dai, Jianrui Cai, Yongbing Zhang, Shu-Tao Xia, Lei Zhang 760 | - Hyperspectral Image Super-Resolution With Optimized RGB Guidance 761 | - Ying Fu, Tao Zhang, Yinqiang Zheng, Debing Zhang, Hua Huang 762 | 763 | ### ICCV19 764 | 765 | - Embedded Block Residual Network: A Recursive Restoration Model for Single-Image Super-Resolution 766 | - Yajun Qiu, Ruxin Wang, Dapeng Tao, Jun Cheng 767 | - Evaluating Robustness of Deep Image Super-Resolution Against Adversarial Attacks 768 | - Jun-Ho Choi, Huan Zhang, Jun-Hyuk Kim, Cho-Jui Hsieh, Jong-Seok Lee 769 | - Kernel Modeling Super-Resolution on Real Low-Resolution Images 770 | - Ruofan Zhou, Sabine Susstrunk 771 | - SROBB: Targeted Perceptual Loss for Single Image Super-Resolution 772 | - Mohammad Saeed Rad, Behzad Bozorgtabar, Urs-Viktor Marti, Max Basler, Hazim Kemal Ekenel, Jean-Philippe Thiran 773 | - Wavelet Domain Style Transfer for an Effective Perception-Distortion Tradeoff in Single Image Super-Resolution 774 | - Xin Deng, Ren Yang, Mai Xu, Pier Luigi Dragotti 775 | - Toward Real-World Single Image Super-Resolution: A New Benchmark and a New Model 776 | - Jianrui Cai, Hui Zeng, Hongwei Yong, Zisheng Cao, Lei Zhang 777 | - RankSRGAN: Generative Adversarial Networks With Ranker for Image Super-Resolution 778 | - Wenlong Zhang, Yihao Liu, Chao Dong, Yu Qiao 779 | - Progressive Fusion Video Super-Resolution Network via Exploiting Non-Local Spatio-Temporal Correlations 780 | - Peng Yi, Zhongyuan Wang, Kui Jiang, Junjun Jiang, Jiayi Ma 781 | - Deep SR-ITM: Joint Learning of Super-Resolution and Inverse Tone-Mapping for 4K UHD HDR Applications 782 | - Soo Ye Kim, Jihyong Oh, Munchurl Kim 783 | - Embedded Block Residual Network: A Recursive Restoration Model for Single-Image Super-Resolution 784 | - Yajun Qiu, Ruxin Wang, Dapeng Tao, Jun Cheng 785 | - Perceptual Deep Depth Super-Resolution 786 | - Oleg Voynov, Alexey Artemov, Vage Egiazarian, Alexander Notchenko, Gleb Bobrovskikh, Evgeny Burnaev, Denis Zorin 787 | - Two-Stream Action Recognition-Oriented Video Super-Resolution 788 | - Haochen Zhang, Dong Liu, Zhiwei Xiong 789 | - Guided Super-Resolution As Pixel-to-Pixel Transformation 790 | - Riccardo de Lutio, Stefano D'Aronco, Jan Dirk Wegner, Konrad Schindler 791 | - Better to Follow, Follow to Be Better: Towards Precise Supervision of Feature Super-Resolution for Small Object Detection 792 | - Junhyug Noh, Wonho Bae, Wonhee Lee, Jinhwan Seo, Gunhee Kim 793 | 794 | ### BMVC19 795 | 796 | - Joint Multi-view Texture Super-resolution and Intrinsic Decomposition 797 | - Gated Multiple Feedback Network for Image Super-Resolution 798 | - Wide Activation for Efficient Image and Video Super-Resolution 799 | - Progressive Face Super-Resolution via Attention to Facial Landmark 800 | - Single Image Super-Resolution via CNN Architectures and TV-TV Minimization 801 | 802 | ### ACM MM19 803 | 804 | - FGLmser: A Flexible GAN-Lmser for Super-Resolution 805 | - Super Resolution Using Dual Path Connections 806 | 807 | ### ICIP19 808 | 809 | - IMAGE SUPER-RESOLUTION USING CNN OPTIMISED BY SELF-FEATURE LOSS 810 | - Learning a cascade regression for no-reference super-resolution image quality assessment 811 | - MULTI-LEVEL RESIDUAL UP-PROJECTION ACTIVATION NETWORK FOR IMAGE SUPER-RESOLUTION 812 | - LEARNING QUATERNION GRAPH FOR COLOR FACE IMAGE SUPER-RESOLUTION 813 | - NON-LOCAL HIERARCHICAL RESIDUAL NETWORK FOR SINGLE IMAGE SUPER-RESOLUTION 814 | - Guided CycleGAN via Semi-Dual Optimal Transport for Photo-Realistic Face Super-resolution 815 | - JITTERED EXPOSURES FOR LIGHT FIELD SUPER-RESOLUTION 816 | - FAST AND LIGHTWEIGHT IMAGE SUPER-RESOLUTION BASED ON DENSE RESIDUALS TWO-CHANNEL NETWORK 817 | - LEARNING SPATIAL AND SPECTRAL FEATURES VIA 2D-1D GENERATIVE ADVERSARIAL NETWORK FOR HYPERSPECTRAL IMAGE SUPER-RESOLUTION 818 | - FAST SUPER-RESOLUTION IN MRI IMAGES USING PHASE STRETCH TRANSFORM, ANCHORED POINT REGRESSION AND ZERO-DATA LEARNING 819 | - PRED: A PARALLEL NETWORK FOR HANDLING MULTIPLE DEGRADATIONS VIA SINGLE MODEL IN SINGLE IMAGE SUPER-RESOLUTION 820 | - GAN-BASED VIDEO SUPER-RESOLUTION WITH DIRECT REGULARIZED INVERSION OF THE LOW-RESOLUTION FORMATION MODEL 821 | - LEARNED MULTIMODAL CONVOLUTIONAL SPARSE CODING FOR GUIDED IMAGE SUPER-RESOLUTION 822 | - MULTI-FRAME SUPER RESOLUTION WITH DEEP RESIDUAL LEARNING ON FLOW REGISTERED NON-INTEGER PIXEL IMAGES 823 | - SINGLE IMAGE SUPER-RESOLUTION VIA CASCADED PARALLEL MULTISIZE RECEPTIVE FIELD 824 | - IMAGE SUPER-RESOLUTION USING COMPLEX DENSE BLOCK ON GENERATIVE ADVERSARIAL NETWORKS 825 | - IMPROVING SUPER RESOLUTION METHODS VIA INCREMENTAL RESIDUAL LEARNING 826 | - LEARNING SUPER-RESOLUTION COHERENT FACIAL FEATURES USING NONLINEAR MULTISET PLS FOR LOW-RESOLUTION FACE RECOGNITION 827 | 828 | ### ICME19 829 | 830 | - GAN-BASED MULTI-LEVEL MAPPING NETWORK FOR SATELLITE IMAGERY SUPER-RESOLUTION 831 | - RECURSIVE MULTI-STAGE UPSCALING NETWORK WITH DISCRIMINATIVE FUSION FOR SUPER-RESOLUTION 832 | - IMPROVING IMAGE SUPER-RESOLUTION VIA FEATURE RE-BALANCING FUSION 833 | - DIFFICULTY-AWARE IMAGE SUPER RESOLUTION VIA DEEP ADAPTIVE DUAL-NETWORK 834 | - DENSE-CONNECTED RESIDUAL NETWORK FOR VIDEO SUPER-RESOLUTION 835 | - RESIDUAL MAGNIFIER: A DENSE INFORMATION FLOW NETWORK FOR SUPER RESOLUTION 836 | - JOINTLY SOLVING DEBLURRING AND SUPER-RESOLUTION PROBLEMS WITH DUAL SUPER- VISED NETWORK 837 | 838 | ### AAAI20 839 | 840 | - Facial Attribute Capsules for Noise Face Super Resolution 841 | 842 | ### arxiv 843 | 844 | - Densely Residual Laplacian Super-Resolution 845 | - Saeed Anwar, Nick Barnes 846 | - Trinity of Pixel Enhancement: a Joint Solution for Demosaicking, Denoising and Super-Resolution 847 | - Guocheng Qian, Jinjin Gu, Jimmy S. Ren, Chao Dong, Furong Zhao, Juan Lin 848 | - Exemplar Guided Face Image Super-Resolution without Facial Landmarks 849 | - Berk Dogan, Shuhang Gu, Radu Timofte 850 | - Suppressing Model Overfitting for Image Super-Resolution Networks 851 | - Ruicheng Feng, Jinjin Gu, Yu Qiao, Chao Dong 852 | - Hybrid Function Sparse Representation towards Image Super Resolution 853 | - Junyi Bian, Baojun Lin, Ke Zhang 854 | - Ensemble Super-Resolution with A Reference Dataset 855 | - Junjun Jiang, Yi Yu, Zheng Wang, Suhua Tang, Ruimin Hu, Jiayi Ma 856 | - Adapting Image Super-Resolution State-of-the-arts and Learning Multi-model Ensemble for Video Super-Resolution 857 | - Chao Li, Dongliang He, Xiao Liu, Yukang Ding, Shilei Wen 858 | - Joint High Dynamic Range Imaging and Super-Resolution from a Single Image 859 | - Jae Woong Soh, Jae Sung Park, Nam Ik Cho 860 | - Super-resolution based generative adversarial network using visual perceptual loss function 861 | - Xuan Zhu, Yue Cheng, Rongzhi Wang 862 | - Multi-scale deep neural networks for real image super-resolution 863 | - Shangqi Gao, Xiahai Zhuang 864 | - Adaptive Transform Domain Image Super-resolution Via Orthogonally Regularized Deep Networks 865 | - Tiantong Guo, Hojjat S. Mousavi, Vishal Monga 866 | - Label Super Resolution with Inter-Instance Loss 867 | - Maozheng Zhao, Le Hou, Han Le, Dimitris Samaras, Nebojsa Jojic, Danielle Fassler, Tahsin Kurc, Rajarsi Gupta, Kolya Malkin, Shahira Abousamra, Shroyer Kenneth, Joel Saltz 868 | - Fast Spatio-Temporal Residual Network for Video Super-Resolution 869 | - Sheng Li, Fengxiang He, Bo Du, Lefei Zhang, Yonghao Xu, Dacheng Tao 870 | - Lightweight Image Super-Resolution with Adaptive Weighted Learning Network 871 | - Chaofeng Wang, Zheng Li, Jun Shi 872 | - The dual approach to non-negative super-resolution: impact on primal reconstruction accuracy 873 | - Stephane Chretien, Andrew Thompson, Bogdan Toader 874 | - Guided Super-Resolution as a Learned Pixel-to-Pixel Transformation 875 | - Riccardo de Lutio, Stefano D'Aronco, Jan Dirk Wegner, Konrad Schindler 876 | - Toward Real-World Single Image Super-Resolution: A New Benchmark and A New Model 877 | - Jianrui Cai, Hui Zeng, Hongwei Yong, Zisheng Cao, Lei Zhang 878 | - SRDGAN: learning the noise prior for Super Resolution with Dual Generative Adversarial Networks 879 | - Jingwei Guan, Cheng Pan, Songnan Li, Dahai Yu 880 | - A Matrix-in-matrix Neural Network for Image Super Resolution 881 | - Hailong Ma, Xiangxiang Chu, Bo Zhang, Shaohua Wan, Bo Zhang 882 | - Robust Super-Resolution GAN, with Manifold-based and Perception Loss 883 | - Uddeshya Upadhyay, Suyash P. Awate 884 | 885 | ### CVPR20 886 | 887 | - Unpaired Image Super-Resolution Using Pseudo-Supervision 888 | - Learning to Have an Ear for Face Super-Resolution 889 | - Correction Filter for Single Image Super-Resolution: Robustifying Off-the-Shelf Deep Super-Resolvers 890 | - Light Field Spatial Super-Resolution via Deep Combinatorial Geometry Embedding and Structural Consistency Regularization 891 | - Residual Feature Aggregation Network for Image Super-Resolution 892 | - Explorable Super Resolution 893 | - Unsupervised Adaptation Learning for Hyperspectral Imagery Super-Resolution 894 | - Deep Unfolding Network for Image Super-Resolution 895 | - TDAN: Temporally-Deformable Alignment Network for Video Super-Resolution 896 | - Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution 897 | - Meta-Transfer Learning for Zero-Shot Super-Resolution 898 | - Dual Super-Resolution Learning for Semantic Segmentation 899 | - Closed-Loop Matters: Dual Regression Networks for Single Image Super-Resolution 900 | - Deep Face Super-Resolution With Iterative Collaboration Between Attentive Recovery and Landmark Estimation 901 | - Channel Attention Based Iterative Residual Learning for Depth Map Super-Resolution 902 | - Image Super-Resolution With Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining 903 | - Learning Texture Transformer Network for Image Super-Resolution 904 | - Structure-Preserving Super Resolution With Gradient Guidance 905 | - Video Super-Resolution With Temporal Group Attention 906 | - EventSR: From Asynchronous Events to Image Reconstruction, Restoration, and Super-Resolution via End-to-End Adversarial Learning 907 | - Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New Strategy 908 | - Robust Reference-Based Super-Resolution With Similarity-Aware Deformable Convolution 909 | - Unified Dynamic Convolutional Network for Super-Resolution With Variational Degradations 910 | 911 | ### ECCV20 912 | 913 | - Binarized Neural Network for Single Image Super Resolution 914 | - Feature Representation Matters: End-to-End Learning for Reference-based Image Super-resolution 915 | - Semantic Object Prediction and Spatial Sound Super-Resolution with Binaural Sounds 916 | - Face Super-Resolution Guided by 3D Facial Priors 917 | - SRFlow: Learning the Super-Resolution Space with Normalizing Flow 918 | - Across Scales & Across Dimensions: Temporal Super-Resolution using Deep Internal Learning 919 | - AR-Net: Adaptive Frame Resolution for Efficient Action Recognition 920 | - Texture Hallucination for Large-Factor Painting Super-Resolution 921 | - Component Divide-and-Conquer for Real-World Image Super-Resolution 922 | - 3D Human Shape and Pose from a Single Low-Resolution Image with Self-Supervised Learning 923 | - MuCAN: Multi-Correspondence Aggregation Network for Video Super-Resolution 924 | - Scene Text Image Super-resolution in the wild 925 | - CooGAN: A Memory-Efficient Framework for High-Resolution Facial Attribute Editing 926 | - Single Image Super-Resolution via a Holistic Attention Network 927 | - Video Super-Resolution with Recurrent Structure-Detail Network 928 | - PlugNet: Degradation Aware Scene Text Recognition Supervised by a Pluggable Super-Resolution Unit 929 | - Resolution Switchable Networks for Runtime Efficient Image Recognition 930 | - Generate to Adapt: Resolution Adaption Network for Surveillance Face Recognition 931 | - Stochastic Frequency Masking to Improve Super-Resolution and Denoising Networks 932 | - Zero-Shot Image Super-Resolution with Depth Guided Internal Degradation Learning 933 | - High-Resolution Image Inpainting with Iterative Confidence Feedback and Guided Upsampling 934 | - LatticeNet: Towards Lightweight Image Super-resolution with Lattice Block 935 | - Spatial-Angular Interaction for Light Field Image Super-Resolution 936 | - VarSR: Variational Super-Resolution Network for Very Low Resolution Images 937 | - Learning with Privileged Information for Efficient Image Super-Resolution 938 | - Towards Content-Independent Multi-Reference Super-Resolution: Adaptive Pattern Matching and Feature Aggregation 939 | - PAMS: Quantized Super-Resolution via Parameterized Max Scale 940 | - Journey Towards Tiny Perceptual Super-Resolution 941 | - Prediction and Recovery for Adaptive Low-Resolution Person Re-Identification 942 | - Mining self-similarity: Label super-resolution with epitomic representations 943 | - Fast Adaptation to Super-Resolution Networks via Meta-Learning 944 | - Cross-Attention in Coupled Unmixing Nets for Unsupervised Hyperspectral Super-Resolution 945 | 946 | ### ACM MM20 947 | 948 | - When Bitstream Prior Meets Deep Prior: Compressed Video Super-resolution with Learning from Decoding 949 | - Space-Time Video Super-Resolution using Temporal Profiles 950 | - When Bitstream Prior Meets Deep Prior: Compressed Video Super-resolution with Learning from Decoding 951 | - Depth Super-Resolution via Deep Controllable Slicing Network 952 | - Light Field Super-resolution via Attention-Guided Fusion of Hybrid Lenses 953 | - Video Super-Resolution using Multi-scale Pyramid 3D Convolutional Networks 954 | - PCA-SRGAN: Incremental Orthogonal Projection Discrimination for Face Super-resolution 955 | - Dual-view Attention Networks for Single Image Super-Resolution 956 | - Towards Lighter and Faster: Learning Wavelets Progressively for Image Super-Resolution 957 | - Space-Time Video Super-Resolution using Temporal Profiles 958 | - Memory Recursive Network for Single Image Super-Resolution 959 | - When Bitstream Prior Meets Deep Prior: Compressed Video Super-resolution with Learning from Decoding 960 | 961 | ## Image Restoration 962 | 963 | ### ICLR19 964 | 965 | - Residual Non-local Attention Networks for Image Restoration 966 | - Yulun Zhang, Kunpeng Li, Kai Li, Bineng Zhong, Yun Fu 967 | - Dynamically Unfolding Recurrent Restorer: A Moving Endpoint Control Method for Image Restoration 968 | - Xiaoshuai Zhang, Yiping Lu, Jiaying Liu, Bin Dong 969 | 970 | ### CVPR19 971 | 972 | - Dual Residual Networks Leveraging the Potential of Paired Operations for Image Restoration 973 | - Xing Liu, Masanori Suganuma, Zhun Sun, Takayuki Okatani 974 | - Attention-Based Adaptive Selection of Operations for Image Restoration in the Presence of Unknown Combined Distortions 975 | - Masanori Suganuma, Xing Liu, Takayuki Okatani 976 | - Modulating Image Restoration With Continual Levels via Adaptive Feature Modification Layers 977 | - Jingwen He, Chao Dong, Yu Qiao 978 | 979 | ### ICCV19 980 | 981 | - CFSNet: Toward a Controllable Feature Space for Image Restoration 982 | - Wei Wang, Ruiming Guo, Yapeng Tian, Wenming Yang 983 | - Fast Image Restoration With Multi-Bin Trainable Linear Units 984 | - Shuhang Gu, Wen Li, Luc Van Gool, Radu Timofte 985 | - Restoration of Non-Rigidly Distorted Underwater Images Using a Combination of Compressive Sensing and Local Polynomial Image Representations 986 | - Jerin Geo James, Pranay Agrawal, Ajit Rajwade 987 | 988 | 989 | ### ICIP19 990 | 991 | - HIGH-DIMENSIONAL EMBEDDING DENOISING AUTOENCODING PRIOR FOR COLOR IMAGE RESTORATION 992 | - JOINT IMAGE RESTORATION AND MATCHING BASED ON HIERARCHICAL SPARSE REPRESENTATION 993 | - UNDERWATER IMAGE SYNTHESIS FROM RGB-D IMAGES AND ITS APPLICATION TO DEEP UNDERWATER IMAGE RESTORATION 994 | 995 | 996 | ### AAAI20 997 | 998 | - Scale-wise Convolution for Image Restoration 999 | 1000 | ### CVPR20 1001 | 1002 | - CycleISP: Real Image Restoration via Improved Data Synthesis 1003 | - Enhanced Blind Face Restoration With Multi-Exemplar Images and Adaptive Spatial Feature Fusion 1004 | - EventSR: From Asynchronous Events to Image Reconstruction, Restoration, and Super-Resolution via End-to-End Adversarial Learning 1005 | - Disparity-Aware Domain Adaptation in Stereo Image Restoration 1006 | - Learning Invariant Representation for Unsupervised Image Restoration 1007 | 1008 | ### ECCV20 1009 | 1010 | - Learning Disentangled Feature Representation for Hybrid-distorted Image Restoration 1011 | - Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation 1012 | - Blind Face Restoration via Deep Multi-scale Component Dictionaries 1013 | - PIPAL: a Large-Scale Image Quality Assessment Dataset for Perceptual Image Restoration 1014 | - Stacking Networks Dynamically for Image Restoration Based on the Plug-and-Play Framework 1015 | - LIRA: Lifelong Image Restoration from Unknown Blended Distortions 1016 | - Interactive Multi-Dimension Modulation with Dynamic Controllable Residual Learning for Image Restoration 1017 | - Microscopy Image Restoration with Deep Wiener-Kolmogorov Filters 1018 | - Fully Trainable and Interpretable Non-Local Sparse Models for Image Restoration 1019 | - Learning Enriched Features for Real Image Restoration and Enhancement 1020 | 1021 | ### ACM MM20 1022 | 1023 | - Attention Cube Network for Image Restoration 1024 | 1025 | ### arxiv 1026 | 1027 | 1028 | - Space-variant Generalized Gaussian Regularization for Image Restoration 1029 | - Alessandro Lanza, Serena Morigi, Monica Pragliola, Fiorella Sgallari 1030 | - Image-Adaptive GAN based Reconstruction 1031 | - Shady Abu Hussein, Tom Tirer, Raja Giryes 1032 | - Path-Restore: Learning Network Path Selection for Image Restoration 1033 | - Ke Yu, Xintao Wang, Chao Dong, Xiaoou Tang, Chen Change Loy 1034 | - Deep Likelihood Network for Image Restoration with Multiple Degradations 1035 | - Yiwen Guo, Wangmeng Zuo, Changshui Zhang, Yurong Chen 1036 | - Image Restoration by Combined Order Regularization with Optimal Spatial Adaptation 1037 | - Sanjay Viswanath, Simon de Beco, Maxime Dahan, Muthuvel Arigovindan 1038 | 1039 | --------------------------------------------------------------------------------