GithubHelp home page GithubHelp logo

t-mac-curry / awesome-image-denoising-state-of-the-art Goto Github PK

View Code? Open in Web Editor NEW

This project forked from z-bingo/awesome-image-denoising-state-of-the-art

0.0 1.0 0.0 20 KB

awesome image and video denoising, state of the art networks

awesome-image-denoising-state-of-the-art's Introduction

Awesome Image or Video Denoising Algorithms

Collection of popular and reproducible image denoising works.

I will update the document when I access the new work for image or video denoising. Everyone could remind me to update if you access the latest work.

This collection is based on the summary of wenbihan's work.

Contents

  1. Denoising Algorithms
    1.1 Filter
    1.2 Sparse Coding
    1.3 Effective Prior
    1.4 Low Rank
    1.5 Deep Learning
    1.6 Sparsity and Low-rankness Combined
    1.7 Combined with High-Level Tasks
    1.8 Image Noise Level Estimation
  2. Benchmark and Dataset
    2.1 Novel Benchmark
    2.2 Commonly Used Denoising Dataset
  3. Others
    3.1 Commonly Used Image Quality Metric Code

Denoising Algorithms

Filter

  • NLM [Web] [Code] [PDF]
    • 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] [Code] [PDF]
    • Image restoration by sparse 3D transform-domain collaborative filtering (SPIE Electronic Imaging 2008), Dabov et al.
  • PID [Web] [Code] [PDF]
    • Progressive Image Denoising (TIP 2014), C. Knaus et al.

Sparse Coding

  • KSVD [Web] [Code] [PDF]
    • Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries (TIP 2006), Elad et al.
  • LSSC [Web] [Code] [PDF]
    • Non-local Sparse Models for Image Restoration (ICCV 2009), Mairal et al.
  • NCSR [Web] [Code] [PDF]
    • Nonlocally Centralized Sparse Representation for Image Restoration (TIP 2012), Dong et al.
  • OCTOBOS [Web] [Code] [PDF]
    • Structured Overcomplete Sparsifying Transform Learning with Convergence Guarantees and Applications (IJCV 2015), Wen et al.
  • GSR [Web] [Code] [PDF]
    • Group-based Sparse Representation for Image Restoration (TIP 2014), Zhang et al.
  • TWSC [Web] [Code] [PDF]
    • A Trilateral Weighted Sparse Coding Scheme for Real-World Image Denoising (ECCV 2018), Xu et al.

Effective Prior

  • EPLL [Web] [Code] [PDF]
    • From Learning Models of Natural Image Patches to Whole Image Restoration (ICCV2011), Zoran et al.
  • GHP [[Web]][Code] [PDF]
    • Texture Enhanced Image Denoising via Gradient Histogram Preservation (CVPR2013), Zuo et al.
  • PGPD [[Web]][Code] [PDF]
    • Patch Group Based Nonlocal Self-Similarity Prior Learning for Image Denoising (ICCV 2015), Xu et al.
  • PCLR [[Web]][Code] [PDF]
    • External Patch Prior Guided Internal Clustering for Image Denoising (ICCV 2015), Chen et al. ย 

Low Rank

  • SAIST [Web] [Code by request] [PDF]
    • Nonlocal image restoration with bilateral variance estimation: a low-rank approach (TIP2013), Dong et al.
  • WNNM [Web] [Code] [PDF]
    • Weighted Nuclear Norm Minimization with Application to Image Denoising (CVPR2014), Gu et al.
  • Multi-channel WNNM [Web] [Code] [PDF]
    • Multi-channel Weighted Nuclear Norm Minimization for Real Color Image Denoising (ICCV 2017), Xu et al.

Deep Learning

  • SF [Web] [Code] [PDF]

    • Shrinkage Fields for Effective Image Restoration (CVPR 2014), Schmidt et al.
  • TNRD [Web] [Code] [PDF]

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

    • Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections (NIPS2016), Mao et al.
  • DnCNN [Web] [Code] [PDF]

    • Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (TIP2017), Zhang et al.
  • MemNet [Web] [Code] [PDF]

    • MemNet: A Persistent Memory Network for Image Restoration (ICCV2017), Tai et al.
  • WIN [Web] [Code] [PDF]

    • Learning Pixel-Distribution Prior with Wider Convolution for Image Denoising (Arxiv), Liu et al.
  • F-W Net [Web] [Code] [PDF]

    • L_p-Norm Constrained Coding With Frank-Wolfe Network (Arxiv), Sun et al.
  • NLCNN [Web] [Code] [PDF]

    • Non-Local Color Image Denoising with Convolutional Neural Networks (CVPR 2017), Lefkimmiatis.
  • Deep image prior [Web] [Code] [PDF]

    • Deep Image Prior (CVPR 2018), Ulyanov et al.
  • xUnit [Web] [Code] [PDF]

    • xUnit: Learning a Spatial Activation Function for Efficient Image Restoration (Arxiv), Kligvasser et al.
  • UDNet [Web] [Code] [PDF]

    • Universal Denoising Networks : A Novel CNN Architecture for Image Denoising (CVPR 2018), Stamatios Lefkimmiatis.
  • Wavelet-CNN [Web] [Code] [PDF]

    • Multi-level Wavelet-CNN for Image Restoration (Arxiv), Liu et al.
  • FFDNet [Web] [Code] [PDF]

    • FFDNet: Toward a Fast and Flexible Solution for CNN-Based Image Denoising (TIP), Zhang et al.
  • FC-AIDE [Web] [Code] [PDF]

    • Fully Convolutional Pixel Adaptive Image Denoiser (Arxiv), Cha et al.
  • CBDNet [Web] [Code] [PDF]

    • Toward Convolutional Blind Denoising of Real Photographs (Arxiv), Guo et al.
  • Noise2Noise [Web] [TF Code] [Keras Unofficial Code] [PDF]

    • Noise2Noise: Learning Image Restoration without Clean Data (ICML 2018), Lehtinen et al.
  • UDN [Web] [Code] [PDF]

    • Universal Denoising Networks- A Novel CNN Architecture for Image Denoising (CVPR 2018), Lefkimmiatis.
  • N3 [Web] [Code] [PDF]

    • Neural Nearest Neighbors Networks (NIPS 2018), Plotz et al.
  • NLRN [Web] [Code] [PDF]

    • Non-Local Recurrent Network for Image Restoration (NIPS 2018), Liu et al.
  • KPN [Web] [Code] [PDF]

    • Burst Denoising with Kernel Prediction Networks (CVPR 2018), Ben et al.
  • MKPN [Web] [Code] [PDF]

    • Multi-Kernel Prediction Networks for Denoising of Burst Images (ArXiv 2019), Marinc et al.
  • RFCN [Web] [Code] [PDF] [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 [Web] [Code] [PDF]

    • Image denoising and restoration with CNN-LSTM Encoder Decoder with Direct Attention (ArXiv 2018), Haque et al.
  • GRDN [Web] [Code] [PDF]

    • GRDN: Grouped Residual Dense Network for Real Image Denoising and GAN-based Real-world Noise Modeling (CVPR 2019), Kim et al.
  • Deformable KPN [Web] [Code] [PDF]

    • Learning Deformable Kernels for Image and Video Denoising (ArXiv 2019), Xu et al.
  • BayerUnify BayerAug [Web] [Code] [PDF]

    • Learning Raw Image Denoising With Bayer Pattern Unification and Bayer Preserving Augmentation (CVPR 2019), Liu et al.
  • RDU-UD [Web] [Code] [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 [Web] [Code] [PDF]

    • Real Image Denoising with Feature Attention (ArXiv 2019), Anwar et al.
  • EDVR [Web] [Code] [PDF]

    • EDVR: Video Restoration With Enhanced Deformable Convolutional Networks (CVPR 2019), Wang et al.
  • DVDNet[Web] [Code] [PDF]

    • DVDnet: A Fast Network for Deep Video Denoising (ArXiv 2019), Tassano et al.
  • FastDVDNet [Web] [Code] [An Unofficial PyTorch Code] [PDF]

    • FastDVDnet: Towards Real-Time Video Denoising Without Explicit Motion Estimation (ArXiv 2019), Tassano et al.
  • ViDeNN [Web] [Code] [PDF]

    • ViDeNN: Deep Blind Video Denoising (ArXiv 2019), Calus et al.
  • Multi-Level Wavelet-CNN [Web] [Code] [PDF]

    • Multi-Level Wavelet Convolutional Neural Networks (IEEE Access), Liu et al.
  • PRIDNet [Web] [Code] [PDF]

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

Sparsity and Low-rankness Combined

  • STROLLR-2D [PDF] [Code]
    • 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] [Code]
    • When Image Denoising Meets High-Level Vision Tasks: A Deep Learning Approach (IJCAI 2018), Liu et al.

Image Noise Level Estimation

  • SINLE [PDF] [Code] [Slides]
    • Single-image Noise Level Estimation for Blind Denoising (TIP 2014), Liu et al.
  • CBDNet [Code] [PDF]
    • Toward Convolutional Blind Denoising of Real Photographs (Arxiv), Guo et al.

Benchmark and Dataset

Novel Benchmark

  • ReNOIR [Web] [Data] [PDF]
    • RENOIR - A Dataset for Real Low-Light Image Noise Reduction (Arxiv 2014), Anaya, Barbu.
  • PolyU [Web] [Data] [PDF]
    • Real-world Noisy Image Denoising: A New Benchmark (Arxiv), Xu et al.
  • Nam [Web] [PDF]
    • A Holistic Approach to Cross-Channel Image Noise Modeling and its Application to Image Denoising (CVPR 2016), Nam et al.
  • Darmstadt (DND) [Web] [Data] [PDF]
    • Benchmarking Denoising Algorithms with Real Photographs (CVPR 2017), Plotz et al.
  • SIDD [Web]
    • A High-Quality Denoising Dataset for Smartphone Cameras.

Commonly Used Denoising Dataset

Others

Commonly Used Image Quality Metric Code

awesome-image-denoising-state-of-the-art's People

Contributors

jiaming-liu avatar z-bingo avatar

Watchers

 avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.