前言：
目前姐妹们对“thresholding method”都比较关心，各位老铁们都想要剖析一些“thresholding method”的相关资讯。那么小编同时在网摘上汇集了一些关于“thresholding method””的相关知识，希望大家能喜欢，兄弟们一起来学习一下吧！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.
相关的文章参考
几种信号降噪算法（第一部分）
几种信号降噪算法（第二部分）
机械故障诊断及工业工程故障诊断若干例子（第一篇）
知乎咨询:哥廷根数学学派
算法代码地址，面包多主页：
擅长现代信号处理(改进小波分析系列，改进变分模态分解，改进经验小波变换，改进辛几何模态分解等等)，改进机器学习，改进深度学习，机械故障诊断，改进时间序列分析(金融信号，心电信号，振动信号等)
标签： #thresholding method #2d降噪算法
