└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # reproducible-video-denoising-state-of-the-art 2 | Collection of popular and reproducible video denoising works. 3 | 4 | Criteria: works must have codes available, and the reproducible results which demonstrate promising or state-of-the-art performances for video denoising. 5 | 6 | This format of the collection is similar to [reproducible-image-denoising](https://github.com/wenbihan/reproducible-image-denoising-state-of-the-art) 7 | 8 | Please feel free to contribute to this repo. 9 | 10 | ## Video Denoising Algorithms 11 | 12 | #### Online Methods 13 | 14 | * ReLD [[Web]](https://www.ece.iastate.edu/~hanguo/denoise.html) [[Code]](https://www.ece.iastate.edu/~hanguo/ReLD_Denoising.zip) [[PDF]](https://www.ece.iastate.edu/~namrata/VideoDenoising_SSP16.pdf) 15 | * Video denoising via online sparse and lowrank matrix decomposition (SSP 2016), Guo and Vaswani. 16 | * VIDOSAT [[Web]](https://github.com/wenbihan/vidosat_icip2015) [[Code]](https://github.com/wenbihan/vidosat_icip2015) [[PDF]](https://arxiv.org/pdf/1710.00947.pdf) 17 | * VIDOSAT - High-dimensional Sparsifying Transform Learning for Online Video Restoration (TIP 2019), Wen et al. 18 | 19 | #### Non-Local Methods 20 | 21 | * VBM3D [[Web]](http://www.cs.tut.fi/~foi/GCF-BM3D/) [[Matlab]](http://www.cs.tut.fi/~foi/GCF-BM3D/bm3d_matlab_package_3.0.5.zip) [[Python]](http://www.cs.tut.fi/~foi/GCF-BM3D/bm3d_python_package_3.0.6.tar.gz) [[PDF]](http://www.cs.tut.fi/~foi/GCF-BM3D/VBM3D_EUSIPCO_2007.pdf) 22 | * Video denoising by sparse 3D transform-domain collaborative filtering (EUSIPCO 2007), Dabov et al. 23 | * VBM4D [[Web]](http://www.cs.tut.fi/~foi/GCF-BM3D/) [[Code]](http://www.cs.tut.fi/~foi/GCF-BM3D/VBM4D_v1.zip) [[PDF]](http://www.cs.tut.fi/~foi/papers/VBM4D-TIP-2cols.pdf) 24 | * Video Denoising, Deblocking and Enhancement Through Separable 4-D Nonlocal Spatiotemporal Transforms (TIP 2012), Maggioni et al. 25 | * RNLF [[Web]](http://iop.math.u-bordeaux.fr/?cat=27) [[Code]](https://github.com/csutour/RNLF) [[PDF]](https://hal.archives-ouvertes.fr/hal-00854830v3/document) 26 | * Adaptive regularization of the NL-means: Application to image and video denoising (TIP 2014), Sutour et al. 27 | * SALT [[Web]](http://transformlearning.csl.illinois.edu/) [[Code]](https://github.com/wenbihan/salt_iccv2017) [[PDF]](http://transformlearning.csl.illinois.edu/assets/Bihan/ConferencePapers/BihanICCV2017salt.pdf) 28 | * Joint Adaptive Sparsity and Low-Rankness on the Fly: An Online Tensor Reconstruction Scheme for Video Denoising (ICCV 2017), Wen et al. 29 | 30 | #### Bayesian Approach 31 | 32 | * VNLB [[Web]](https://github.com/pariasm/vnlb) [[Code]](https://github.com/pariasm/vnlb) [[PDF]](https://link.springer.com/article/10.1007/s10851-017-0742-4) 33 | * Video Denoising via Empirical Bayesian Estimation of Space-Time Patches (JMIV 2017), Arias and Morel 34 | 35 | #### Deep Learning 36 | 37 | * VNLNet [[Web]](https://github.com/axeldavy/vnlnet) [[Code]](https://github.com/axeldavy/vnlnet) [[PDF]](https://arxiv.org/pdf/1811.12758.pdf) 38 | * Non-Local Video Denoising by CNN (Arxiv 2018), Davy et al. 39 | * FastDVDnet [[Web]](https://github.com/hsijiaxidian/FOCNet) [[Code]](https://github.com/m-tassano/fastdvdnet) [[PDF]](https://arxiv.org/pdf/1907.01361.pdf) 40 | * FastDVDnet: Towards Real-Time Video Denoising Without Explicit Motion Estimation (Arxiv 2019), Tassano et al. 41 | * LDK [[Web]]() [[Code]](https://github.com/z-bingo/Deformable-Kernels-For-Video-Denoising) [[PDF]](https://arxiv.org/abs/1904.06903) 42 | * Learning Deformable Kernels for Image and Video Denoising (Arxiv 2019), Xu et al. 43 | * EDVR [[Web]](https://xinntao.github.io/projects/EDVR) [[Code]](https://github.com/xinntao/EDVR) [[PDF]](https://arxiv.org/abs/1905.02716) 44 | * Video Restoration with Enhanced Deformable Convolutional Networks (CVPRW 2019), Wang et al. 45 | * TOF [[Web]](http://toflow.csail.mit.edu/) [[Code]](https://github.com/anchen1011/toflow) [[PDF]](http://toflow.csail.mit.edu/toflow_ijcv.pdf) 46 | * Video Enhancement with Task-Oriented Flow (IJCV 2019), Xue et al. 47 | 48 | 49 | #### Model-blind Learning 50 | 51 | * BlindDenoising [[Web]](https://github.com/tehret/blind-denoising) [[Code]](https://github.com/tehret/blind-denoising) [[PDF]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Ehret_Model-Blind_Video_Denoising_via_Frame-To-Frame_Training_CVPR_2019_paper.pdf) 52 | * Model-Blind Video Denoising via Frame-To-Frame Training (CVPR 2019), Tassano et al. 53 | * ViDeNN [[Web]](https://github.com/clausmichele/ViDeNN) [[Code]](https://github.com/clausmichele/ViDeNN) [[PDF]](http://openaccess.thecvf.com/content_CVPRW_2019/papers/NTIRE/Claus_ViDeNN_Deep_Blind_Video_Denoising_CVPRW_2019_paper.pdf) 54 | * ViDeNN: Deep Blind Video Denoising (CVPRW 2019), Claus and Gemert --------------------------------------------------------------------------------