SOTAVerified

Image Denoising

Image Denoising is a computer vision task that involves removing noise from an image. Noise can be introduced into an image during acquisition or processing, and can reduce image quality and make it difficult to interpret. Image denoising techniques aim to restore an image to its original quality by reducing or removing the noise, while preserving the important features of the image.

( Image credit: Wide Inference Network for Image Denoising via Learning Pixel-distribution Prior )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 951–1000 of 1220 papers

TitleStatusHype
Learning Multi-Layer Transform Models—0
DN-ResNet: Efficient Deep Residual Network for Image Denoising—0
No-reference Image Denoising Quality Assessment—0
Deep Learning for Image Denoising: A Survey—0
Image Reconstruction Using Deep Learning—0
Image Denoising and Super-Resolution using Residual Learning of Deep Convolutional Network—0
Enhanced 3DTV Regularization and Its Applications on Hyper-spectral Image Denoising and Compressed Sensing—0
A Brief Review of Real-World Color Image DenoisingCode0
Connecting Image Denoising and High-Level Vision Tasks via Deep LearningCode0
Data Dropout: Optimizing Training Data for Convolutional Neural Networks—0
Deep Boosting for Image Denoising—0
Performance Analysis of Plug-and-Play ADMM: A Graph Signal Processing Perspective—0
Automatic Foreground Extraction from Imperfect Backgrounds using Multi-Agent Consensus Equilibrium—0
PACO: Global Signal Restoration via PAtch COnsensus—0
Class-Aware Fully-Convolutional Gaussian and Poisson DenoisingCode0
Low Rank Regularization: A Review—0
X-GANs: Image Reconstruction Made Easy for Extreme Cases—0
The Power of Complementary Regularizers: Image Recovery via Transform Learning and Low-Rank Modeling—0
Deep Graph Laplacian Regularization for Robust Denoising of Real ImagesCode0
Multi-bin Trainable Linear Unit for Fast Image Restoration Networks—0
U-Finger: Multi-Scale Dilated Convolutional Network for Fingerprint Image Denoising and InpaintingCode0
User Loss -- A Forced-Choice-Inspired Approach to Train Neural Networks directly by User Interaction—0
A salt and pepper noise image denoising method based on the generative classification—0
A Trilateral Weighted Sparse Coding Scheme for Real-World Image Denoising—0
Image Restoration Using Conditional Random Fields and Scale Mixtures of Gaussians—0
Sparse Representation and Non-Negative Matrix Factorization for image denoise—0
Learning Personalized Representation for Inverse Problems in Medical Imaging Using Deep Neural Network—0
Multimodal Image Denoising based on Coupled Dictionary Learning—0
MedGAN: Medical Image Translation using GANs—0
Identifying Recurring Patterns with Deep Neural Networks for Natural Image DenoisingCode0
Non-Local Recurrent Network for Image RestorationCode0
Regularization by Denoising: Clarifications and New InterpretationsCode0
A High-Quality Denoising Dataset for Smartphone Cameras—0
Hyperspectral Image Denoising Employing a Spatial-Spectral Deep Residual Convolutional Neural NetworkCode0
Multi-Scale Weighted Nuclear Norm Image Restoration—0
CocoNet: A deep neural network for mapping pixel coordinates to color valuesCode0
Unsupervised Learning with Stein's Unbiased Risk EstimatorCode0
Backpropagation with N-D Vector-Valued Neurons Using Arbitrary Bilinear Products—0
Rate-Optimal Denoising with Deep Neural Networks—0
Dynamically Unfolding Recurrent Restorer: A Moving Endpoint Control Method for Image Restoration—0
Multi-level Wavelet-CNN for Image RestorationCode0
Image Denoising via Collaborative Dual-Domain Patch Filtering—0
Joint Bilateral Filter for Signal Recovery from Phase Preserved Curvelet Coefficients for Image Denoising—0
Real-world Noisy Image Denoising: A New BenchmarkCode0
Regional Priority Based Anomaly Detection using Autoencoders—0
Multi-scale Processing of Noisy Images using Edge Preservation LossesCode0
SUNLayer: Stable denoising with generative networks—0
Group Sparsity Residual with Non-Local Samples for Image Denoising—0
Nonlocality-Reinforced Convolutional Neural Networks for Image DenoisingCode0
Learning Filter Bank Sparsifying Transforms—0
Show:102550
← PrevPage 20 of 25Next →
All datasetsSIDDDNDELD SonyA7S2 x200SID SonyA7S2 x250ELD SonyA7S2 x100SID x100SID x300SID SonyA7S2 x100urban100 sigma15Urban100 sigma50BSD68 sigma50SID SonyA7S2 x300

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CGNetPSNR (sRGB)40.39—Unverified
2KBNetPSNR (sRGB)40.35—Unverified
3NAFNetPSNR (sRGB)40.3—Unverified
4SSAMANPSNR (sRGB)40.08—Unverified
5PNGANPSNR (sRGB)40.07—Unverified
6RestormerPSNR (sRGB)40.02—Unverified
7HINetPSNR (sRGB)39.99—Unverified
8MAXIM-3SPSNR (sRGB)39.96—Unverified
9Uformer-BPSNR (sRGB)39.89—Unverified
10SRMNetPSNR (sRGB)39.72—Unverified
#ModelMetricClaimedVerifiedStatus
1DualDnPSNR (sRGB)40.59—Unverified
2PNGANPSNR (sRGB)40.18—Unverified
3SSAMANPSNR (sRGB)40.05—Unverified
4RestormerPSNR (sRGB)40.03—Unverified
5Uformer-BPSNR (sRGB)39.98—Unverified
6NBNetPSNR (sRGB)39.89—Unverified
7MIRNetPSNR (sRGB)39.88—Unverified
8MAXIM-3SPSNR (sRGB)39.84—Unverified
9MPRNetPSNR (sRGB)39.8—Unverified
10SADNetPSNR (sRGB)39.59—Unverified
#ModelMetricClaimedVerifiedStatus
1LLD*PSNR (Raw)44.95—Unverified
2PMNPSNR (Raw)44.51—Unverified
3SFRNPSNR (Raw)44.1—Unverified
4LLDPSNR (Raw)43.84—Unverified
5ELDPSNR (Raw)43.43—Unverified
6LRDPSNR (Raw)43.32—Unverified
7Paired Data(SID)PSNR (Raw)41.97—Unverified
8StarlightPSNR (Raw)40.86—Unverified
9ExposureDiffusion (UNet+ELD)PSNR (Raw)40.39—Unverified
10Noise FlowPSNR (Raw)39.23—Unverified
#ModelMetricClaimedVerifiedStatus
1LLD*PSNR (Raw)41.02—Unverified
2PMNPSNR (Raw)40.92—Unverified
3SFRNPSNR (Raw)40.22—Unverified
4LLDPSNR (Raw)39.76—Unverified
5Paired Data (SID)PSNR (Raw)39.6—Unverified
6ELDPSNR (Raw)39.44—Unverified
7LEDPSNR (Raw)39.34—Unverified
8LRDPSNR (Raw)39.25—Unverified
9StarlightPSNR (Raw)36.25—Unverified
10Noise FlowPSNR (Raw)35.8—Unverified
#ModelMetricClaimedVerifiedStatus
1LLD*PSNR (Raw)46.74—Unverified
2PMNPSNR (Raw)46.5—Unverified
3SFRNPSNR (Raw)46.02—Unverified
4LLDPSNR (Raw)45.61—Unverified
5ELDPSNR (Raw)45.45—Unverified
6LRDPSNR (Raw)44.95—Unverified
7Paired Data(SID)PSNR (Raw)44.47—Unverified
8StarlightPSNR (Raw)43.8—Unverified
9Noise FlowPSNR (Raw)41.05—Unverified
#ModelMetricClaimedVerifiedStatus
1LLD*PSNR (Raw)43.36—Unverified
2PMNPSNR (Raw)43.16—Unverified
3SFRNPSNR (Raw)42.29—Unverified
4LLDPSNR (Raw)42.1—Unverified
5SID (paired real data)PSNR (Raw)42.06—Unverified
6ELDPSNR (Raw)41.95—Unverified
7StarlightPSNR (Raw)40.47—Unverified
8Noise FlowPSNR (Raw)38.89—Unverified
#ModelMetricClaimedVerifiedStatus
1LLD*PSNR (Raw)37.8—Unverified
2PMNPSNR (Raw)37.77—Unverified
3SFRNPSNR (Raw)36.87—Unverified
4Paired Data(SID)PSNR (Raw)36.85—Unverified
5LLDPSNR (Raw)36.76—Unverified
6ELDPSNR (Raw)36.36—Unverified
7StarlightPSNR (Raw)32.99—Unverified
8Noise FlowPSNR (Raw)32.29—Unverified
#ModelMetricClaimedVerifiedStatus
1PMNPSNR (Raw)43.16—Unverified
2SFRNPSNR (Raw)42.29—Unverified
3LEDPSNR (Raw)41.98—Unverified
4ELDPSNR (Raw)41.95—Unverified
5LRDPSNR (Raw)41.95—Unverified
#ModelMetricClaimedVerifiedStatus
1AKDTAverage PSNR35.64—Unverified
2MaIR+PSNR35.42—Unverified
3MaIRPSNR35.35—Unverified
4SCUNet SCUNetAverage PSNR35.18—Unverified
#ModelMetricClaimedVerifiedStatus
1MaIR+PSNR30.41—Unverified
2MaIRPSNR30.3—Unverified
3SCUNet SCUNetPSNR30.14—Unverified
4AKDTPSNR29.82—Unverified
#ModelMetricClaimedVerifiedStatus
1MaIR+PSNR30.08—Unverified
2MaIRPSNR28.66—Unverified
#ModelMetricClaimedVerifiedStatus
1LEDPSNR (Raw)36.67—Unverified
2LRDPSNR (Raw)36.03—Unverified
#ModelMetricClaimedVerifiedStatus
1MaIR+PSNR33.3—Unverified
2MaIRPSNR33.22—Unverified
#ModelMetricClaimedVerifiedStatus
1R3LPSNR27.67—Unverified
#ModelMetricClaimedVerifiedStatus
1BRGMLPIPS0.24—Unverified
#ModelMetricClaimedVerifiedStatus
1BRGMLPIPS0.24—Unverified
#ModelMetricClaimedVerifiedStatus
1ExposureDiffusion (UNet+paired data)PSNR (Raw)36.82—Unverified
#ModelMetricClaimedVerifiedStatus
1PNGANPSNR40.78—Unverified
#ModelMetricClaimedVerifiedStatus
1PNGANPSNR40.55—Unverified
#ModelMetricClaimedVerifiedStatus
1absDLODRMSE0.07—Unverified