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:
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- SINLE [PDF] [Code] [Slides]
- Single-image Noise Level Estimation for Blind Denoising (TIP 2014), Liu et al.
- 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.
- PSNR (Peak Signal-to-Noise Ratio) [Wiki] [Matlab Code] [Python Code]
- SSIM (Structural similarity) [Wiki] [Matlab Code] [Python Code]
- NIQE (Naturalness Image Quality Evaluator) [Web] [Matlab Code] [Python Code]
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