└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # Awesome Haze Removal [![Awesome](https://awesome.re/badge-flat.svg)](https://awesome.re) 2 | 3 | A curated list of related resources for haze removal. 4 | 5 | ## Table of Contents 6 | - [Papers](#papers) 7 | - [Datasets](#datasets) 8 | 9 | ## Papers 10 | 11 | ### arXiv 12 | 13 | - Night Time Haze and Glow Removal using Deep Dilated Convolutional Network [[paper](https://arxiv.org/pdf/1902.00855.pdf)] 14 | - Single Image Haze Removal using a Generative Adversarial Network [[paper](https://arxiv.org/pdf/1810.09479.pdf)] 15 | - Does Haze Removal Help CNN-based Image Classification? [[paper](https://arxiv.org/pdf/1810.05716.pdf)] 16 | - Underwater Image Haze Removal and Color Correction with an Underwater-ready Dark Channel Prior [[paper](https://arxiv.org/pdf/1807.04169.pdf)] 17 | - Deep joint rain and haze removal from single images [[paper](https://arxiv.org/pdf/1801.06769.pdf)] 18 | - CANDY: Conditional Adversarial Networks based Fully End-to-End System for Single Image Haze Removal [[paper](https://arxiv.org/ftp/arxiv/papers/1801/1801.02892.pdf)] 19 | - Learning Aggregated Transmission Propagation Networks for Haze Removal and Beyond [[paper](https://arxiv.org/pdf/1711.06787.pdf)] 20 | 21 | ### 2020 22 | 23 | - Fast Deep Multi-patch Hierarchical Network for Nonhomogeneous Image Dehazing (**CVPR**) [[paper](https://arxiv.org/pdf/2005.05999.pdf)] 24 | - FD-GAN: Generative Adversarial Networks with Fusion-discriminator for Single Image Dehazing (**AAAI**) [[paper](https://arxiv.org/pdf/2001.06968.pdf)] 25 | - PMHLD: Patch Map Based Hybrid Learning DehazeNet for Single Image Haze Removal (**TIP**) [[paper](https://ieeexplore.ieee.org/document/9094006)][[code](https://github.com/weitingchen83/Dehazing-PMHLD-Patch-Map-Based-Hybrid-Learning-DehazeNet-for-Single-Image-Haze-Removal-TIP-2020)] 26 | - Single Image Haze Removal using a Generative Adversarial Network (**WiSPNET**) [[paper](https://arxiv.org/ftp/arxiv/papers/1810/1810.09479.pdf)][[code](https://github.com/thatbrguy/Dehaze-GAN)] 27 | 28 | ### 2019 29 | 30 | - Single Image Dehazing with a Generic Model-Agnostic Convolutional Neural Network (**IEEE SPL**) [[paper](https://ieeexplore.ieee.org/document/8686264)][[code](https://github.com/Seanforfun/GMAN_Net_Haze_Removal)] 31 | - PMS-Net: Robust Haze Removal Based on Patch Map for Single Images (**CVPR**) [[paper](https://openaccess.thecvf.com/content_CVPR_2019/papers/Chen_PMS-Net_Robust_Haze_Removal_Based_on_Patch_Map_for_Single_CVPR_2019_paper.pdf)][[code](https://github.com/weitingchen83/PMS-Net)] 32 | 33 | ### 2018 34 | 35 | - A Light Dual-Task Neural Network for Haze Removal (**IEEE SPL**) [[paper](https://arxiv.org/pdf/1904.06024.pdf)] 36 | - DR-Net: Transmission Steered Single Image Dehazing Network with Weakly Supervised Refinement (**CVPR**) [[paper](https://arxiv.org/pdf/1712.00621.pdf)] 37 | - C2MSNet: A Novel approach for single image haze removal (**WACV**) [[paper](https://arxiv.org/pdf/1801.08406.pdf)] 38 | - Multiple Linear Regression Haze-removal Model Based on Dark Channel Prior (**IEEE CPS**) [[paper](https://arxiv.org/pdf/1904.11587.pdf)] 39 | 40 | ### 2017 41 | 42 | - AOD-Net: All-in-One Dehazing Network (**ICCV**) [[paper](https://openaccess.thecvf.com/content_ICCV_2017/papers/Li_AOD-Net_All-In-One_Dehazing_ICCV_2017_paper.pdf)] 43 | - Fast Haze Removal for Nighttime Image Using Maximum Reflectance Prior (**CVPR**) [[paper](https://chaimi2013.github.io/Research/NighttimeDehazing/index.html)][[code](https://github.com/chaimi2013/MRP)] 44 | - Joint Transmission Map Estimation and Dehazing using Deep Networks (**IEEE TCSVT**) [[paper](https://arxiv.org/pdf/1708.00581.pdf)] 45 | - Deep fully convolutional regression networks for single image haze removal (**VCIP**) [[paper](https://ieeexplore.ieee.org/document/8305035)][[code](https://github.com/AlphaNext/DFCRN-for-Image-Dehazing)] 46 | 47 | ### 2016 48 | 49 | - Non-local Image Dehazing (**CVPR**) [[paper](https://openaccess.thecvf.com/content_cvpr_2016/papers/Berman_Non-Local_Image_Dehazing_CVPR_2016_paper.pdf)][[code](https://github.com/danaberman/non-local-dehazing)] 50 | - Single Image Dehazing via Multi-scale Convolutional Neural Networks (**ECCV**) [[paper](http://www.eccv2016.org/files/posters/P-1B-14.pdf)] 51 | - DehazeNet: An End-to-End System for Single Image Haze Removal (**IEEE TIP**) [[paper](https://arxiv.org/pdf/1601.07661.pdf)][[code](https://github.com/caibolun/DehazeNet)] 52 | 53 | ### 2015 54 | 55 | - A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior (**IEEE TIP**) [[paper](https://core.ac.uk/reader/41073066)][[code](https://github.com/JiamingMai/Color-Attenuation-Prior-Dehazing)] 56 | 57 | ### 2014 58 | 59 | - Efficient Image Dehazing with Boundary Constraint and Contextual Regularization (**ICCV**) [[paper](https://openaccess.thecvf.com/content_iccv_2013/papers/Meng_Efficient_Image_Dehazing_2013_ICCV_paper.pdf)][[code](https://github.com/gfmeng/imagedehaze)] 60 | - Nighttime Haze Removal Based on a New Imaging Model (**ICIP**) [[paper](https://chaimi2013.github.io/Research/NighttimeDehazing_ICIP2014/NighttimeDehazing_ICIP2014/NighttimeDehazing_ICIP2014.pdf)][[code](https://github.com/chaimi2013/NighttimeDehaze)] 61 | 62 | ### 2009 63 | 64 | - Single Image Haze Removal Using Dark Channel Prior (**CVPR**) [[paper](http://www.lsis.org/cipa-uwp/article/biblio/dehaze_cvpr2009_SingleImageHazeRemovalUsingDarkChannelPrior.pdf)][[code-Python](https://github.com/He-Zhang/image_dehaze)][[code-MATLAB](https://github.com/sjtrny/Dark-Channel-Haze-Removal)][[code-C++](https://github.com/MagicRock100/Single-Image-Haze-Removal-Using-Dark-Channel-Prior)] 65 | 66 | ### 2008 67 | 68 | - Single Image Dehazing (**ACM TOG**) [[paper](https://www.cs.huji.ac.il/~raananf/papers/defog.pdf?origin=publication_detail)][[homepage](https://www.cs.huji.ac.il/~raananf/projects/defog/)] 69 | 70 | ## Datasets 71 | 72 | ## Contact 73 | 74 | For any questions regard this repository, please directly contact Xin Zeng (work.xzeng@gmail.com). 75 | 76 | ## License 77 | 78 | To the extent possible under law, [Xin Zeng](https://github.com/zengxin1020) has retained all copyright and related or neighboring rights to this work. 79 | --------------------------------------------------------------------------------