图像降噪相关论文-从传统方法到深度学习

Filter

· NLM [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 [PDF]

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

· PID [PDF]

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

Sparse Coding

· KSVD [PDF]

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

· LSSC [PDF]

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

· NCSR [PDF]

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

· OCTOBOS [PDF]

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

· GSR [PDF]

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

· TWSC [PDF]

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

Effective Prior

· EPLL [PDF]

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

· GHP [PDF]

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

· PGPD [PDF]

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

· PCLR [PDF]

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

Low Rank

· SAIST [PDF]

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

· WNNM [PDF]

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

· Multi-channel WNNM [PDF]

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

Deep Learning

· SF [PDF]

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

· TNRD [PDF]

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

· RED [PDF]

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

· DnCNN [PDF]

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

· MemNet [PDF]

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

· WIN [PDF]

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

· F-W Net [PDF]

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

· NLCNN [PDF]

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

· Deep image prior [PDF]

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

· xUnit [PDF]

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

· UDNet] [PDF]

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

· Wavelet-CNN [PDF]

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

· FFDNet [PDF]

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

· FC-AIDE [PDF]

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

· CBDNet [PDF]

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

· Noise2Noise [PDF]

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

· Neighbor2Neighbor [PDF]

o Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Images, Huang et al.

· UDN [PDF]

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

· N3 [PDF]

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

· NLRN [PDF]

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

· KPN [PDF]

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

· MKPN [PDF]

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

· RFCN [PDF] [PDF]

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

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

· CNN-LSTM [PDF]

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

· GRDN [PDF]

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

· Deformable KPN [PDF]

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

· BayerUnify BayerAug [PDF]

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

· RDU-UD [PDF]

o 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]

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

· EDVR [PDF]

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

· DVDNet [PDF]

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

· FastDVDNet [Web] [Code] [An Unofficial PyTorch Code] [PDF]

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

· ViDeNN [PDF]

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

· Multi-Level Wavelet-CNN [PDF]

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

· PRIDNet [PDF]

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

· CycleISP [PDF]

o CycleISP: Real Image Restoration via Improved Data Synthesis (CVPR 2020), Zamir et al.

· MIRNEt [PDF]

o MIRNEt: Learning Enriched Features for Real Image Restoration and Enhancement (ECCV 2020), Zamir et al.

Sparsity and Low-rankness Combined

· STROLLR-2D [PDF]

o 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]

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

Image Noise Level Estimation

· SINLE [PDF]

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

· CBDNet [PDF]

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

· HyperIQA[PDF]

o Blindly Assess Image Quality in the Wild Guided by A Self-Adaptive Hyper Network (CVPR 2020), Su et al.

· PaQ-2-PiQ [PDF]

o From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality (Arxiv), Ying et al.

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博士，担任《Mechanical System and Signal Processing》审稿专家，担任
《中国电机工程学报》优秀审稿专家，《控制与决策》，《系统工程与电子技术》等EI期刊审稿专家，担任《计算机科学》，《电子器件》 ， 《现代制造过程》 ，《船舶工程》 ，《轴承》 ，《工矿自动化》 ，《重庆理工大学学报》 ，《噪声与振动控制》 ，《机械传动》 ，《机械强度》 ，《机械科学与技术》 ，《机床与液压》，《声学技术》，《应用声学》，《石油机械》，《西安工业大学学报》等中文核心审稿专家。
擅长领域：现代信号处理，机器学习，深度学习，数字孪生，时间序列分析，设备缺陷检测、设备异常检测、设备智能故障诊断与健康管理PHM等。