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 4701–4725 of 7282 papers

TitleStatusHype
Learning Fully Convolutional Networks for Iterative Non-blind Deconvolution—0
LEARNING GENERATIVE MODELS FOR DEMIXING OF STRUCTURED SIGNALS FROM THEIR SUPERPOSITION USING GANS—0
Learning Generative Models of Structured Signals from Their Superposition Using GANs with Application to Denoising and Demixing—0
Learning Generic Diffusion Processes for Image Restoration—0
Learning Gradually Non-convex Image Priors Using Score Matching—0
TMPQ-DM: Joint Timestep Reduction and Quantization Precision Selection for Efficient Diffusion Models—0
Learning Implicit Brain MRI Manifolds with Deep Learning—0
Learning in Confusion: Batch Active Learning with Noisy Oracle—0
Learning Integrodifferential Models for Image Denoising—0
Learning Lipschitz-Controlled Activation Functions in Neural Networks for Plug-and-Play Image Reconstruction Methods—0
Learning local regularization for variational image restoration—0
TOCH: Spatio-Temporal Object-to-Hand Correspondence for Motion Refinement—0
ToddlerDiffusion: Interactive Structured Image Generation with Cascaded Schrödinger Bridge—0
Learning Mixtures of Gaussians Using the DDPM Objective—0
Learning Model-Blind Temporal Denoisers without Ground Truths—0
Learning Multi-Layer Transform Models—0
Learning Multiple Visual Tasks while Discovering their Structure—0
To Dereverb Or Not to Dereverb? Perceptual Studies On Real-Time Dereverberation Targets—0
Learning Multi-scale Spatial-frequency Features for Image Denoising—0
Learning Neural Light Transport—0
Learning Nonlinear Spectral Filters for Color Image Reconstruction—0
Learning Non-local Image Diffusion for Image Denoising—0
Learning Personalized Representation for Inverse Problems in Medical Imaging Using Deep Neural Network—0
Learning quadrangulated patches for 3D shape parameterization and completion—0
Learning Quadrangulated Patches For 3D Shape Processing—0
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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