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图像去噪是指减少数字图像中噪声的过程。随着深度学习的发展，也有许多深度学习方法被用于图像/视频去噪。本文整理了一些去噪算法与数据集。

Denoising Algorithms

Filter

NLM

  • [Web]:https://sites.google.com/site/shreyamsha/publications/image-denoising-based-on-nlfmt

  • [Code]:https://www.mathworks.com/matlabcentral/fileexchange/44090-image-denoising-based-on-non-local-means-filter-and-its-method-noise-thresholding?focused=3806802&tab=function

  • [PDF]:https://link.springer.com/article/10.1007/s11760-012-0389-y)

  • A non-local algorithm for image denoising (CVPR 05), Buades et al.

  • Image denoising based on non-local means filter and its method noise thresholding (SIVP2013), B. Kumar

BM3D

  • [Web]:http://www.cs.tut.fi/~foi/GCF-BM3D/

  • [Code]:http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D.zip

  • [PDF]:http://www.cs.tut.fi/~foi/GCF-BM3D/SPIE08_deblurring.pdf

  • Image restoration by sparse 3D transform-domain collaborative filtering (SPIE Electronic Imaging 2008), Dabov et al.

PID

  • [Web]:http://www.cgg.unibe.ch/publications/progressive-image-denoising

  • [Code]:http://www.cgg.unibe.ch/publications/progressive-image-denoising/pid.zip

  • [PDF]:http://www.cgg.unibe.ch/publications/2014/progressive-image-denoising/at_download/file)

  • Progressive Image Denoising (TIP 2014), C. Knaus et al.

Sparse Coding

KSVD

  • [Web]:http://www.cs.technion.ac.il/~ronrubin/software.html

  • [Code]:https://github.com/jbhuang0604/SelfSimSR/tree/master/Lib/KSVD

  • [PDF]:http://www.egr.msu.edu/~aviyente/elad06.pdf

  • Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries (TIP 2006), Elad et al.

LSSC

  • [Web]:https://lear.inrialpes.fr/people/mairal/

  • [Code]:https://lear.inrialpes.fr/people/mairal/resources/denoise_ICCV09.tar.gz

  • [PDF]:http://www.di.ens.fr/~fbach/iccv09_mairal.pdf

  • Non-local Sparse Models for Image Restoration (ICCV 2009), Mairal et al.

NCSR

  • [Web]:http://www4.comp.polyu.edu.hk/~cslzhang/NCSR.htm

  • [Code]:http://www4.comp.polyu.edu.hk/~cslzhang/code/NCSR.rar

  • [PDF]:http://www4.comp.polyu.edu.hk/~cslzhang/paper/NCSR_TIP_final.pdf

  • Nonlocally Centralized Sparse Representation for Image Restoration (TIP 2012), Dong et al.

OCTOBOS

  • [Web]:http://transformlearning.csl.illinois.edu/projects/

  • [Code]:https://github.com/wenbihan/octobos_IJCV2016

  • [PDF]:http://transformlearning.csl.illinois.edu/assets/Sai/JournalPapers/SaiBihanIJCV2014OCTOBOS.pdf

  • Structured Overcomplete Sparsifying Transform Learning with Convergence Guarantees and Applications (IJCV 2015), Wen et al.

GSR

  • [Web]:https://jianzhang.tech/projects/GSR/

  • [Code]:http://csjianzhang.github.io/codes/GSR_Code_Package_3.0.zip

  • [PDF]:http://csjianzhang.github.io/papers/TIP2014_single.pdf

  • Group-based Sparse Representation for Image Restoration (TIP 2014), Zhang et al.

TWSC

  • [Web]:https://github.com/csjunxu/TWSC-ECCV2018

  • [Code]:https://github.com/csjunxu/TWSC-ECCV2018

  • [PDF]:http://openaccess.thecvf.com/content_ECCV_2018/papers/XU_JUN_A_Trilateral_Weighted_ECCV_2018_paper.pdf

  • A Trilateral Weighted Sparse Coding Scheme for Real-World Image Denoising (ECCV 2018), Xu et al.

Effective Prior

EPLL

  • [Web]:https://people.csail.mit.edu/danielzoran/

  • [Code]:https://people.csail.mit.edu/danielzoran/epllcode.zip

  • [PDF]:http://people.ee.duke.edu/~lcarin/EPLICCVCameraReady.pdf

  • From Learning Models of Natural Image Patches to Whole Image Restoration (ICCV2011), Zoran et al.

GHP

  • Web]:https://github.com/tingfengainiaini/GHPBasedImageRestoration

  • [PDF]:https://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Zuo_Texture_Enhanced_Image_2013_CVPR_paper.pdf

  • Texture Enhanced Image Denoising via Gradient Histogram Preservation (CVPR2013), Zuo et al.

PGPD

  • [Web]:https://github.com/csjunxu/PGPD_Offline_BID

  • [PDF]:http://www4.comp.polyu.edu.hk/~cslzhang/paper/PGPD.pdf

  • Patch Group Based Nonlocal Self-Similarity Prior Learning for Image Denoising (ICCV 2015), Xu et al.

PCLR

  • [Web]:http://www4.comp.polyu.edu.hk/~cslzhang/code/PCLR.zip

  • [PDF]:http://www4.comp.polyu.edu.hk/~cslzhang/paper/PCLR.pdf

  • External Patch Prior Guided Internal Clustering for Image Denoising (ICCV 2015), Chen et al.

Low Rank

SAIST

  • [Web]:http://see.xidian.edu.cn/faculty/wsdong/wsdong_Publication.htm[Code by request]

  • [PDF]:http://see.xidian.edu.cn/faculty/wsdong/Papers/Journal/TIP_LASSC.pdf

  • Nonlocal image restoration with bilateral variance estimation: a low-rank approach (TIP2013), Dong et al.

WNNM

  • [Web]:https://sites.google.com/site/shuhanggu/home

  • [Code]:http://www4.comp.polyu.edu.hk/~cslzhang/code/WNNM_code.zip

  • [PDF]:https://pdfs.semanticscholar.org/6d55/6272625b672ba54b5ab3d9e6474088a4b78f.pdf

  • Weighted Nuclear Norm Minimization with Application to Image Denoising (CVPR2014), Gu et al.

Multi-channel WNNM

  • [Web]:http://www4.comp.polyu.edu.hk/~csjunxu/Publications.html

  • [Code]:http://www4.comp.polyu.edu.hk/~csjunxu/code/MCWNNM.zip

  • [PDF]:http://www4.comp.polyu.edu.hk/~csjunxu/paper/MCWNNM.pdf

  • Multi-channel Weighted Nuclear Norm Minimization for Real Color Image Denoising (ICCV 2017), Xu et al.

Deep Learning

SF

  • [Web]:http://www.visinf.tu-darmstadt.de/vi_research/code/index.en.jsp#shrinkage_fields

  • [Code]:https://github.com/uschmidt83/shrinkage-fields

  • [PDF]:http://research.uweschmidt.org/pubs/cvpr14schmidt.pdf

  • Shrinkage Fields for Effective Image Restoration (CVPR 2014), Schmidt et al.

TNRD

  • [Web]:http://www.icg.tugraz.at/Members/Chenyunjin/about-yunjin-chen

  • [Code]:https://www.dropbox.com/s/8j6b880m6ddxtee/TNRD-Codes.zip?dl=0

  • [PDF]:https://arxiv.org/pdf/1508.02848.pdf

  • Trainable nonlinear reaction diffusion: A flexible framework for fast and effective image restoration (TPAMI 2016), Chen et al.

RED

  • [Web]:https://bitbucket.org/chhshen/image-denoising/

  • [Code]:https://bitbucket.org/chhshen/image-denoising/

  • [PDF]:https://arxiv.org/pdf/1603.09056.pdf

  • Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections (NIPS2016), Mao et al.

DnCNN

  • [Web]:https://github.com/cszn/DnCNN

  • [Code]:https://github.com/cszn/DnCNN

  • [PDF]:https://arxiv.org/pdf/1608.03981v1.pdf

  • Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (TIP2017), Zhang et al.

MemNet

  • [Web]:https://github.com/tyshiwo/MemNet

  • [Code]:https://github.com/tyshiwo/MemNet

  • [PDF]:http://cvlab.cse.msu.edu/pdfs/Image_Restoration%20using_Persistent_Memory_Network.pdf

  • MemNet: A Persistent Memory Network for Image Restoration (ICCV2017), Tai et al.

WIN

  • [Web]:https://github.com/cswin/WIN

  • [Code]:https://github.com/cswin/WIN

  • [PDF]:https://arxiv.org/pdf/1707.09135.pdf

  • Learning Pixel-Distribution Prior with Wider Convolution for Image Denoising (Arxiv), Liu et al.

F-W Net

  • [Web]:https://github.com/sunke123/FW-Net

  • [Code]:https://github.com/sunke123/FW-Net

  • [PDF]:https://arxiv.org/abs/1802.10252)

  • L_p-Norm Constrained Coding With Frank-Wolfe Network (Arxiv), Sun et al.

NLCNN

  • [Web]:https://cig.skoltech.ru/publications

  • [Code]:https://github.com/cig-skoltech/NLNet

  • [PDF]:http://www.skoltech.ru/app/data/uploads/sites/19/2017/06/1320.pdf

  • Non-Local Color Image Denoising with Convolutional Neural Networks (CVPR 2017), Lefkimmiatis.

Deep image prior

  • [Web]:https://dmitryulyanov.github.io/deep_image_prior

  • [Code]:https://github.com/DmitryUlyanov/deep-image-prior

  • [PDF]:https://sites.skoltech.ru/app/data/uploads/sites/25/2018/04/deep_image_prior.pdf

  • Deep Image Prior (CVPR 2018), Ulyanov et al.

xUnit

  • [Web]:https://github.com/kligvasser/xUnit

  • [Code]:https://github.com/kligvasser/xUnit

  • [PDF]:https://arxiv.org/pdf/1711.06445.pdf

  • xUnit: Learning a Spatial Activation Function for Efficient Image Restoration (Arxiv), Kligvasser et al.

UDNet

  • [Web]:https://github.com/cig-skoltech/UDNet

  • [Code]:https://github.com/cig-skoltech/UDNet

  • [PDF]:https://arxiv.org/pdf/1711.07807.pdf

  • Universal Denoising Networks : A Novel CNN Architecture for Image Denoising (CVPR 2018), Stamatios  Lefkimmiatis.

Wavelet-CNN

  • [Web]:https://github.com/lpj0/MWCNN

  • [Code]:https://github.com/lpj0/MWCNN

  • [PDF]:https://arxiv.org/abs/1805.07071)

  • Multi-level Wavelet-CNN for Image Restoration (Arxiv), Liu et al.

FFDNet

  • [Web]:https://github.com/cszn/FFDNet/

  • [Code]:https://github.com/cszn/FFDNet/

  • [PDF]:https://arxiv.org/abs/1710.04026)

  • FFDNet: Toward a Fast and Flexible Solution for CNN-Based Image Denoising (TIP), Zhang et al.

FC-AIDE

  • [Web]:https://github.com/csm9493/FC-AIDE

  • [Code]:https://github.com/GuoShi28/CBDNet

  • [PDF]:https://arxiv.org/pdf/1807.07569.pd

  • Fully Convolutional Pixel Adaptive Image Denoiser (Arxiv), Cha et al.

CBDNet

  • [Web]:https://github.com/GuoShi28/CBDNet

  • [Code]:https://github.com/GuoShi28/CBDNet

  • [PDF]:https://arxiv.org/pdf/1807.04686.pdf

  • Toward Convolutional Blind Denoising of Real Photographs (Arxiv), Guo et al.

Noise2Noise

  • [Web]:https://github.com/yu4u/noise2noise[TF Code]:https://github.com/NVlabs/noise2noise[Keras Unofficial Code]:https://github.com/yu4u/noise2noise

  • [PDF]:https://arxiv.org/pdf/1803.04189.pdf

  • Noise2Noise: Learning Image Restoration without Clean Data (ICML 2018), Lehtinen et al.

UDN

  • [Web]:https://cig.skoltech.ru/publications

  • [Code]:https://github.com/cig-skoltech/UDNet

  • [PDF]:http://www.skoltech.ru/app/data/uploads/sites/19/2018/03/UDNet_CVPR2018.pdf

  • Universal Denoising Networks- A Novel CNN Architecture for Image Denoising (CVPR 2018), Lefkimmiatis.

N3

  • [Web]:https://github.com/visinf/n3net

  • [Code]:https://github.com/visinf/n3net

  • [PDF]:https://arxiv.org/abs/1810.12575

  • Neural Nearest Neighbors Networks (NIPS 2018), Plotz et al.

NLRN

  • [Web]:https://github.com/Ding-Liu/NLRN

  • [Code]:https://github.com/Ding-Liu/NLRN

  • [PDF]:https://arxiv.org/pdf/1806.02919.pdf

  • Non-Local Recurrent Network for Image Restoration (NIPS 2018), Liu et al.

KPN

  • [PDF]:https://arxiv.org/pdf/1712.02327.pdf

  • Burst Denoising with Kernel Prediction Networks (CVPR 2018), Ben et al.

MKPN

  • [PDF]:https://arxiv.org/pdf/1902.05392.pdf

  • Multi-Kernel Prediction Networks for Denoising of Burst Images (ArXiv 2019), Marinc et al.

RFCN

  • [Web]:http://visual.cs.ucl.ac.uk/pubs/deepBurstDenoising/

  • [PDF]:https://arxiv.org/pdf/1712.05790.pdf

  • [PDF]:https://arxiv.org/pdf/1904.07483.pdf

  • Deep Burst Denoising (ArXiv 2017), Clement et al.

  • End-to-End Denoising of Dark Burst Images Using Recurrent Fully Convolutional Networks (ArXiv 2019), Zhao et al.

CNN-LSTM

  • [PDF]:https://arxiv.org/pdf/1801.05141.pdf

  • Image denoising and restoration with CNN-LSTM Encoder Decoder with Direct Attention (ArXiv 2018), Haque et al.

GRDN

  • [PDF]:https://arxiv.org/pdf/1905.11172.pdf

  • GRDN: Grouped Residual Dense Network for Real Image Denoising and GAN-based Real-world Noise Modeling (CVPR 2019), Kim et al.

Deformable KPN

  • [PDF]:https://arxiv.org/pdf/1904.06903.pdf

  • Learning Deformable Kernels for Image and Video Denoising (ArXiv 2019), Xu et al.

BayerUnify BayerAug :

  • [Code]:https://github.com/Jiaming-Liu/BayerUnifyAug

  • [PDF]:http://openaccess.thecvf.com/content_CVPRW_2019/papers/NTIRE/Liu_Learning_Raw_Image_Denoising_With_Bayer_Pattern_Unification_and_Bayer_CVPRW_2019_paper.pdf

  • Learning Raw Image Denoising With Bayer Pattern Unification and Bayer Preserving Augmentation (CVPR 2019), Liu et al.

RDU-UD

  • [PDF]:http://openaccess.thecvf.com/content_CVPRW_2019/papers/NTIRE/Sim_A_Deep_Motion_Deblurring_Network_Based_on_Per-Pixel_Adaptive_Kernels_CVPRW_2019_paper.pdf

  • A Deep Motion Deblurring Network Based on Per-Pixel Adaptive Kernels With Residual Down-Up and Up-Down Modules (CVPR 2019), Sim et al.

RIDNet

  • [PDF]:https://arxiv.org/pdf/1904.07396.pdf

  • Real Image Denoising with Feature Attention (ArXiv 2019), Anwar et al.

EDVR

  • [Web]:https://github.com/xinntao/EDVR

  • [Code]:https://github.com/xinntao/EDVR

  • [PDF]:http://openaccess.thecvf.com/content_CVPRW_2019/papers/NTIRE/Wang_EDVR_Video_Restoration_With_Enhanced_Deformable_Convolutional_Networks_CVPRW_2019_paper.pdf

  • EDVR: Video Restoration With Enhanced Deformable Convolutional Networks (CVPR 2019), Wang et al.

DVDNet

  • [Web]:https://github.com/m-tassano/dvdnet

  • [Code]:https://github.com/m-tassano/dvdnet

  • [PDF]:https://arxiv.org/pdf/1906.11890.pdf

  • DVDnet: A Fast Network for Deep Video Denoising (ArXiv 2019), Tassano et al.

FastDVDNet

  • [Web]:https://github.com/m-tassano/fastdvdnet

  • [Code]:https://github.com/m-tassano/fastdvdnet[An Unofficial PyTorch Code]:https://github.com/z-bingo/FastDVDNet

  • [PDF]:https://arxiv.org/pdf/1907.01361.pdf

  • FastDVDnet: Towards Real-Time Video Denoising Without Explicit Motion Estimation (ArXiv 2019), Tassano et al.

ViDeNN

  • [Web]:https://github.com/z-bingo/awesome-image-denoising-state-of-the-art/blob/master

  • [Code]:https://github.com/z-bingo/awesome-image-denoising-state-of-the-art/blob/master

  • [PDF]:https://arxiv.org/pdf/1904.10898.pdf

  • ViDeNN: Deep Blind Video Denoising (ArXiv 2019), Calus et al.

Multi-Level Wavelet-CNN

  • [Web]:https://github.com/z-bingo/awesome-image-denoising-state-of-the-art/blob/master

  • [Code]:https://github.com/z-bingo/awesome-image-denoising-state-of-the-art/blob/master

  • [PDF]:https://arxiv.org/pdf/1907.03128.pdf

  • Multi-Level Wavelet Convolutional Neural Networks (IEEE Access), Liu et al.

PRIDNet

  • [Web]:https://github.com/z-bingo/awesome-image-denoising-state-of-the-art/blob/master

  • [Code]:https://github.com/z-bingo/awesome-image-denoising-state-of-the-art/blob/master

  • [PDF]:https://arxiv.org/pdf/1908.00273.pdf

  • Pyramid Read Image Denoising Network (Arxiv 2019), Zhao et al.

Sparsity and Low-rankness Combined

  • STROLLR-2D

  • [PDF]:http://transformlearning.csl.illinois.edu/assets/Bihan/ConferencePapers/BihanICASSP2017strollr.pdf

  • [Code]:https://github.com/wenbihan/strollr2d_icassp2017)

  • When Sparsity Meets Low-Rankness: Transform Learning With Non-Local Low-Rank Constraint for Image Restoration (ICASSP 2017), Wen et al.

Combined with High-Level Tasks

  • Meets High-level Tasks

  • [PDF]:https://arxiv.org/pdf/1706.04284.pdf

  • [Code]:https://github.com/wenbihan/DeepDenoising)

  • When Image Denoising Meets High-Level Vision Tasks: A Deep Learning Approach (IJCAI 2018), Liu et al.

Image Noise Level Estimation

SINLE

  • [PDF]:http://www.ok.sc.e.titech.ac.jp/res/NLE/TIP2013-noise-level-estimation06607209.pdf

  • [Code]:https://www.mathworks.com/matlabcentral/fileexchange/36921-noise-level-estimation-from-a-single-image[Slides]:https://wwwpub.zih.tu-dresden.de/~hh3/Hauptsem/SS16/noise.pdf

  • Single-image Noise Level Estimation for Blind Denoising (TIP 2014), Liu et al.

CBDNet

  • [Code]:https://github.com/GuoShi28/CBDNet

  • [PDF]:https://arxiv.org/pdf/1807.04686.pdf

  • Toward Convolutional Blind Denoising of Real Photographs (Arxiv), Guo et al.

Benchmark and Dataset

Novel Benchmark

  • ReNOIR

  • [Web]:http://ani.stat.fsu.edu/~abarbu/Renoir.html[Data]:http://ani.stat.fsu.edu/~abarbu/Renoir.html

  • [PDF]:https://arxiv.org/pdf/1409.8230.pdf

  • RENOIR - A Dataset for Real Low-Light Image Noise Reduction (Arxiv 2014), Anaya, Barbu.

PolyU

  • [Web]:https://github.com/csjunxu/PolyU-Real-World-Noisy-Images-Dataset[Data]:https://github.com/csjunxu/PolyU-Real-World-Noisy-Images-Dataset

  • [PDF]:https://arxiv.org/pdf/1804.02603.pdf

  • Real-world Noisy Image Denoising: A New Benchmark (Arxiv), Xu et al.

Nam

  • [Web]:http://snam.ml/research/ccnoise

  • [PDF]:http://snam.ml/assets/ccnoise_cvpr16/ccnoise_cvpr16.pdf

  • A Holistic Approach to Cross-Channel Image Noise Modeling and its Application to Image Denoising (CVPR 2016), Nam et al.

Darmstadt (DND

  • [Web]:https://noise.visinf.tu-darmstadt.de/[Data]:https://noise.visinf.tu-darmstadt.de/downloads/

  • [PDF]:https://download.visinf.tu-darmstadt.de/papers/2017-cvpr-ploetz-benchmarking_denoising_algorithms-preprint.pdf

  • Benchmarking Denoising Algorithms with Real Photographs (CVPR 2017), Plotz et al.

SIDD

  • [Web]:https://www.eecs.yorku.ca/~kamel/sidd/dataset.php

  • A High-Quality Denoising Dataset for Smartphone Cameras.

Commonly Used Denoising Dataset

Kodak

  • [Web]:http://r0k.us/graphics/kodak/

USC SIPI-Misc

  • [Web]:http://sipi.usc.edu/database/database.php?volume=misc

BSD

  • [Web]:https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/

Vimeo-90K

  • [Web]:http://toflow.csail.mit.edu/

Adobe-5K

  • [Web]:https://data.csail.mit.edu/graphics/fivek/

OpenImage

  • [Web]:http://storage.googleapis.com/openimages/web/index.html

Others

Commonly Used Image Quality Metric Code

PSNR (Peak Signal-to-Noise Ratio

  • [Wiki]:https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio

  • [Matlab Code]:https://www.mathworks.com/help/images/ref/psnr.html

  • [Python Code]:https://github.com/aizvorski/video-quality

SSIM (Structural similarity

  • [Wiki]:https://en.wikipedia.org/wiki/Structural_similarity

  • [Matlab Code]:http://www.cns.nyu.edu/~lcv/ssim/ssim_index.m

  • [Python Code]:https://github.com/aizvorski/video-quality/blob/master/ssim.py

NIQE (Naturalness Image Quality Evaluator

  • [Web]:http://live.ece.utexas.edu/research/Quality/nrqa.htm

  • [Matlab Code]:http://live.ece.utexas.edu/research/Quality/nrqa.htm

  • [Python Code]:https://github.com/aizvorski/video-quality/blob/master/niqe.py

原文链接：https://github.com/z-bingo/awesome-image-denoising-state-of-the-art

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