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 6526–6550 of 7282 papers

TitleStatusHype
STINR: Deciphering Spatial Transcriptomics via Implicit Neural Representation—0
Diffusion Model Patching via Mixture-of-Prompts—0
Diffusion Models-Aided Uplink Channel Estimation for RIS-Assisted Systems—0
All-optical image denoising using a diffractive visual processor—0
All-in-one Multi-degradation Image Restoration Network via Hierarchical Degradation Representation—0
Diffusion Models are Secretly Exchangeable: Parallelizing DDPMs via Autospeculation—0
Diffusion Models as Cartoonists! The Curious Case of High Density Regions—0
STITCH-OPE: Trajectory Stitching with Guided Diffusion for Off-Policy Evaluation—0
All-in-One Deep Learning Framework for MR Image Reconstruction—0
ST-LDM: A Universal Framework for Text-Grounded Object Generation in Real Images—0
STLLM-DF: A Spatial-Temporal Large Language Model with Diffusion for Enhanced Multi-Mode Traffic System Forecasting—0
stMCDI: Masked Conditional Diffusion Model with Graph Neural Network for Spatial Transcriptomics Data Imputation—0
Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence—0
Stochastic Deep Restoration Priors for Imaging Inverse Problems—0
Stochastic Digital Twin for Copy Detection Patterns—0
Video super-resolution for single-photon LIDAR—0
Diffusion Models For Multi-Modal Generative Modeling—0
Diffusion Models for Tabular Data Imputation and Synthetic Data Generation—0
Diffusion Models for Unsupervised Anomaly Detection in Fetal Brain Ultrasound—0
Diffusion Models in Bioinformatics: A New Wave of Deep Learning Revolution in Action—0
Aligning Protein Conformation Ensemble Generation with Physical Feedback—0
Diffusion Models in De Novo Drug Design—0
Aligning Foundation Model Priors and Diffusion-Based Hand Interactions for Occlusion-Resistant Two-Hand Reconstruction—0
SGD Jittering: A Training Strategy for Robust and Accurate Model-Based Architectures—0
Diffusion Models to Enhance the Resolution of Microscopy Images: A Tutorial—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