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reproducible-image-denoising-state-of-the-art

Collection of popular and reproducible single image denoising works. This collection is inspired by the summary by flyywh

Criteria: works must have codes available, and the reproducible results demonstrate state-of-the-art performances.

Check out the following collections of reproducible state-of-the-art algorithms:

Denoising Algorithms (AWGN)

Filtering

  • 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.

Classical External Priors

  • 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.  

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 Denoising

  • 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.
  • NLCNN [Web] [Code] [PDF]
    • Non-Local Color Image Denoising with Convolutional Neural Networks (CVPR 2017), Lefkimmiatis.
  • xUnit [Web] [Code] [PDF]
    • xUnit: Learning a Spatial Activation Function for Efficient Image Restoration (CVPR 2018), Kligvasser et al.
  • UDNet [Web] [Code] [PDF]
    • Universal Denoising Networks : A Novel CNN Architecture for Image Denoising (CVPR 2018), Lefkimmiatis.
  • Wavelet-CNN [Web] [Code] [PDF]
    • Multi-level Wavelet-CNN for Image Restoration (CVPR 2018), Liu et al.
  • IRN [Web] [Code] [PDF]
    • Deep Image Demosaicking using a Cascade of Convolutional Residual Denoising Networks (ECCV 2018), Lefkimmiatis.
  • FFDNet [Web] [Code] [PDF]
    • FFDNet: Toward a Fast and Flexible Solution for CNN-Based Image Denoising (TIP 2018), Zhang 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.
  • RDN+ [Web] [Code] [PDF]
    • Residual Dense Network for Image Restoration (CVPR 2018), Zhang et al.
  • FC-AIDE [Web] [Code] [PDF]
    • Fully Convolutional Pixel Adaptive Image Denoiser (ICCV 2019), Cha et al.
  • FOCNet [Web] [Code] [PDF]
    • FOCNet: A Fractional Optimal Control Network for Image Denoising (CVPR 2019), Jia et al.

Unsupervised / Weakly-Supervised Deep Denoising

  • Noise2Noise [Web] [TF Code] [Keras Unofficial Code] [PDF]
    • Noise2Noise: Learning Image Restoration without Clean Data (ICML 2018), Lehtinen et al.
  • DIP [Web] [Code] [PDF]
    • Deep Image Prior (CVPR 2018), Ulyanov et al.
  • Noise2Void [Web] [Code] [PDF]
    • Learning Denoising from Single Noisy Images (CVPR 2019), Krull et al.
  • Noise2Self [Web] [Code] [PDF]
    • Noise2Self: Blind Denoising by Self-Supervision (ICML 2019), Batson and Royer
  • Self-Supervised Denoising [Web] [Code] [PDF]
    • High-Quality Self-Supervised Deep Image Denoising (NIPS 2019), Laine et al.

Hybrid Model for Denoising

  • STROLLR [PDF] [Code]
    • When Sparsity Meets Low-Rankness: Transform Learning With Non-Local Low-Rank Constraint for Image Restoration (ICASSP 2017), Wen et al.
  • Meets High-level Tasks [PDF] [Code]
    • When Image Denoising Meets High-Level Vision Tasks: A Deep Learning Approach (IJCAI 2018), Liu et al.
  • USA [PDF] [Code]
    • Segmentation-aware Image Denoising Without Knowing True Segmentation (Arxiv), Wang et al.

Blind Denoising or Real Noise Removal

  • RIDNet [Web] [Code] [PDF]
    • Real Image Denoising with Feature Attention (ICCV 2019), Anwar and Barnes.
  • CBDNet [Web] [Code] [PDF]
    • Toward Convolutional Blind Denoising of Real Photographs (CVPR 2019), Guo et al.
  • VDNNet [Web] [Code] [PDF]
    • Variational Denoising Network: Toward Blind Noise Modeling and Removal (NIPS 2019), Yue et al.

Image Noise Level Estimation

  • SINLE [PDF] [Code] [Slides]
    • Single-image Noise Level Estimation for Blind Denoising (TIP 2014), Liu et al.

Novel Real Denoising Benchmark

  • ReNOIR [Web] [Data] [PDF]
    • RENOIR - A Dataset for Real Low-Light Image Noise Reduction (Arxiv 2014), Anaya, Barbu.
  • Darmstadt [Web] [Data] [PDF]
    • Benchmarking Denoising Algorithms with Real Photographs (CVPR 2017), Tobias Plotz, Stefan Roth.
  • PolyU [Web] [Data] [PDF]
    • Real-world Noisy Image Denoising: A New Benchmark (Arxiv), Xu et al.
  • SIDD [Web] [Data] [PDF]
    • A High-Quality Denoising Dataset for Smartphone Cameras (CV{R 2018), Abdelhamed et al.

Commonly Used Denoising Dataset

Commonly Used Image Quality Metrics


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