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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1976–2000 of 7282 papers

TitleStatusHype
Learning to Assimilate in Chaotic Dynamical SystemsCode0
Learning to Denoise Biomedical Knowledge Graph for Robust Molecular Interaction PredictionCode0
Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving AugmentationCode0
Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging ProblemsCode0
Learning Robust 3D Representation from CLIP via Dual DenoisingCode0
Learning parametric dictionaries for graph signalsCode0
Deep Sparse and Low-Rank Prior for Hyperspectral Image DenoisingCode0
Learning Pixel-Distribution Prior with Wider Convolution for Image DenoisingCode0
Learning normalized image densities via dual score matchingCode0
Deep sound-field denoiser: optically-measured sound-field denoising using deep neural networkCode0
Learning of Patch-Based Smooth-Plus-Sparse Models for Image ReconstructionCode0
Learning Priors in High-frequency Domain for Inverse Imaging ReconstructionCode0
Diagnosis and Prognosis of Faults in High-Speed Aeronautical Bearings with a Collaborative Selection Incremental Deep Transfer Learning ApproachCode0
Leveraging Deep Stein's Unbiased Risk Estimator for Unsupervised X-ray DenoisingCode0
DeepSign: Deep Learning for Automatic Malware Signature Generation and ClassificationCode0
Learning Instance-Specific Parameters of Black-Box Models Using Differentiable SurrogatesCode0
DeepSat - A Learning framework for Satellite ImageryCode0
(Almost) Smooth Sailing: Towards Numerical Stability of Neural Networks Through Differentiable Regularization of the Condition NumberCode0
Learning in Deep Factor Graphs with Gaussian Belief PropagationCode0
Learning Joint Denoising, Demosaicing, and Compression from the Raw Natural Image Noise DatasetCode0
Deep Retinex Decomposition for Low-Light EnhancementCode0
CURL: Neural Curve Layers for Global Image EnhancementCode0
BRSR-OpGAN: Blind Radar Signal Restoration using Operational Generative Adversarial NetworkCode0
Deep Residual Autoencoders for Expectation Maximization-inspired Dictionary LearningCode0
Adaptive Mixing of Auxiliary Losses in Supervised LearningCode0
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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