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 4701–4750 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
Learning Representations of Affect from Speech—0
Token Caching for Diffusion Transformer Acceleration—0
Learning Robust Representations with Graph Denoising Policy Network—0
Learning robust speech representation with an articulatory-regularized variational autoencoder—0
Wavelet based multivariate signal denoising using Mahalanobis distance and EDF statistics—0
Learning Sparse Adversarial Dictionaries For Multi-Class Audio Classification—0
Learning Sparse Graph Laplacian with K Eigenvector Prior via Iterative GLASSO and Projection—0
Learning Sparse Graphs Under Smoothness Prior—0
Learning Sparse Latent Representations for Generator Model—0
Learning Sparsity-Promoting Regularizers using Bilevel Optimization—0
Learning Spatial Adaptation and Temporal Coherence in Diffusion Models for Video Super-Resolution—0
ToMCAT: Theory-of-Mind for Cooperative Agents in Teams via Multiagent Diffusion Policies—0
Learning Spatial Features from Audio-Visual Correspondence in Egocentric Videos—0
Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels—0
Learning Structure-Guided Diffusion Model for 2D Human Pose Estimation—0
Learning the Night Sky with Deep Generative Priors—0
Learning the Structure for Structured Sparsity—0
Learning to Aggregate and Refine Noisy Labels for Visual Sentiment Analysis—0
A Deep Learning based Detection Method for Combined Integrity-Availability Cyber Attacks in Power System—0
Learning to Clean: A GAN Perspective—0
Wavelet-based Topological Loss for Low-Light Image Denoising—0
Tongji University Undergraduate Team for the VoxCeleb Speaker Recognition Challenge2020—0
Learning to Distill: The Essence Vector Modeling Framework—0
Wavelet Denoising and Attention-based RNN-ARIMA Model to Predict Forex Price—0
Learning to Efficiently Sample from Diffusion Probabilistic Models—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