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 2026–2050 of 7282 papers

TitleStatusHype
Learning of Patch-Based Smooth-Plus-Sparse Models for Image ReconstructionCode0
Noisy Batch Active Learning with Deterministic AnnealingCode0
Periodic Materials Generation using Text-Guided Joint Diffusion ModelCode0
Deep Neural Networks Motivated by Partial Differential EquationsCode0
Learning in Deep Factor Graphs with Gaussian Belief PropagationCode0
Learning Generative Models using Denoising Density EstimatorsCode0
Learning Instance-Specific Parameters of Black-Box Models Using Differentiable SurrogatesCode0
Learning parametric dictionaries for graph signalsCode0
Learning to Denoise Biomedical Knowledge Graph for Robust Molecular Interaction PredictionCode0
Mesh Denoising with Facet Graph ConvolutionsCode0
Learning Deep Representations Using Convolutional Auto-encoders with Symmetric Skip ConnectionsCode0
Learning Deep CNN Denoiser Prior for Image RestorationCode0
Deep Contrastive Patch-Based Subspace Learning for Camera Image Signal ProcessingCode0
Back-Projection based Fidelity Term for Ill-Posed Linear Inverse ProblemsCode0
Learning-Based Reconstruction of FRI SignalsCode0
Alioth: A Machine Learning Based Interference-Aware Performance Monitor for Multi-Tenancy Applications in Public CloudCode0
Learning Better Masking for Better Language Model Pre-trainingCode0
Learning Dynamics of Linear Denoising AutoencodersCode0
Learned Convolutional Sparse CodingCode0
Deep Mean-Shift Priors for Image RestorationCode0
Learned D-AMP: Principled Neural Network based Compressive Image RecoveryCode0
Lateral Connections in Denoising Autoencoders Support Supervised LearningCode0
Accelerated First Order Methods for Variational ImagingCode0
Layered Rendering Diffusion Model for Controllable Zero-Shot Image SynthesisCode0
A Weighted Difference of Anisotropic and Isotropic Total Variation for Relaxed Mumford-Shah Color and Multiphase Image SegmentationCode0
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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