SOTAVerified

Denoising

Denoising is a task in image processing and computer vision that aims to remove or reduce noise from an image. Noise can be introduced into an image due to various reasons, such as camera sensor limitations, lighting conditions, and compression artifacts. The goal of denoising is to recover the original image, which is considered to be noise-free, from a noisy observation.

( Image credit: Beyond a Gaussian Denoiser )

Papers

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Showing 6926–6950 of 7282 papers

TitleStatusHype
Multi-pretrained Deep Neural Network—0
Multi-View Treelet Transform—0
A Holistic Approach to Cross-Channel Image Noise Modeling and Its Application to Image Denoising—0
Needle-Match: Reliable Patch Matching Under High Uncertainty—0
Minimizing the Maximal Rank—0
Multispectral Images Denoising by Intrinsic Tensor Sparsity Regularization—0
From Noise Modeling to Blind Image Denoising—0
Training Auto-encoders Effectively via Eliminating Task-irrelevant Input Variables—0
Total Variation Classes Beyond 1d: Minimax Rates, and the Limitations of Linear Smoothers—0
Simultaneous Sparse Dictionary Learning and Pruning—0
Neural Universal Discrete Denoiser—0
Image Restoration with Locally Selected Class-Adapted Models—0
A Geometric Approach to Color Image Regularization—0
Hierarchical Piecewise-Constant Super-regions—0
Biologically Inspired Radio Signal Feature Extraction with Sparse Denoising Autoencoders—0
Automatic Image Annotation via Label Transfer in the Semantic Space—0
A Gaussian Mixture MRF for Model-Based Iterative Reconstruction with Applications to Low-Dose X-ray CT—0
Modified Weibull distribution for Biomedical signals denoising—0
Transport Analysis of Infinitely Deep Neural Network—0
Plug-and-Play ADMM for Image Restoration: Fixed Point Convergence and Applications—0
Improved Sparse Low-Rank Matrix Estimation—0
Graph Laplacian Regularization for Image Denoising: Analysis in the Continuous Domain—0
Efficient Dictionary Learning with Sparseness-Enforcing Projections—0
Learning to Generate Genotypes with Neural Networks—0
VConv-DAE: Deep Volumetric Shape Learning Without Object LabelsCode0
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All datasetsDarmstadt Noise DatasetDIV2KDNDiris

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SINDyPSNR81—Unverified
2Pixel-shuffling DownsamplingPSNR38.4—Unverified
3TWSCPSNR37.93—Unverified
4CBDNet(Syn)PSNR37.57—Unverified
5MCWNNMPSNR37.38—Unverified
6Han et alPSNR35.95—Unverified
7FFDNetPSNR34.4—Unverified
8TNRDPSNR33.65—Unverified
9CDnCNN-BPSNR32.43—Unverified
10NLRNPSNR30.8—Unverified
#ModelMetricClaimedVerifiedStatus
1DRUnet_Poisson_0.01Average PSNR (dB)33.92—Unverified
#ModelMetricClaimedVerifiedStatus
1DRANetAverage PSNR39.64—Unverified
#ModelMetricClaimedVerifiedStatus
1PCNN+RL+HMEAverage84.61—Unverified