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 2351–2375 of 7282 papers

TitleStatusHype
Can Diffusion Models Learn Hidden Inter-Feature Rules Behind Images?—0
How vulnerable is my policy? Adversarial attacks on modern behavior cloning policies—0
TruePose: Human-Parsing-guided Attention Diffusion for Full-ID Preserving Pose Transfer—0
Data denoising with self consistency, variance maximization, and the Kantorovich dominance—0
Taking a Big Step: Large Learning Rates in Denoising Score Matching Prevent Memorization—0
Controllable Satellite-to-Street-View Synthesis with Precise Pose Alignment and Zero-Shot Environmental Control—0
SLCGC: A lightweight Self-supervised Low-pass Contrastive Graph Clustering Network for Hyperspectral Images—0
Exploring the latent space of diffusion models directly through singular value decomposition—0
Towards Consistent and Controllable Image Synthesis for Face Editing—0
InterLCM: Low-Quality Images as Intermediate States of Latent Consistency Models for Effective Blind Face Restoration—0
Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity—0
A generative foundation model for an all-in-one seismic processing framework—0
CoDe: Blockwise Control for Denoising Diffusion ModelsCode0
Sparse Measurement Medical CT Reconstruction using Multi-Fused Block Matching Denoising Priors—0
Semantic Communication based on Generative AI: A New Approach to Image Compression and Edge Optimization—0
Personalized Denoising Implicit Feedback for Robust Recommender SystemCode0
Patch Triplet Similarity Purification for Guided Real-World Low-Dose CT Image Denoising—0
CAT Pruning: Cluster-Aware Token Pruning For Text-to-Image Diffusion ModelsCode0
Beyond Fixed Horizons: A Theoretical Framework for Adaptive Denoising Diffusions—0
Ambient Denoising Diffusion Generative Adversarial Networks for Establishing Stochastic Object Models from Noisy Image Data—0
Spend Wisely: Maximizing Post-Training Gains in Iterative Synthetic Data BoostrappingCode0
Principal Components for Neural Network InitializationCode0
CoSTI: Consistency Models for (a faster) Spatio-Temporal ImputationCode0
PSyDUCK: Training-Free Steganography for Latent Diffusion—0
Collaborative Diffusion Model for Recommender System—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