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 2876–2900 of 7282 papers

TitleStatusHype
Conditional Lagrangian Wasserstein Flow for Time Series Imputation—0
MotionAura: Generating High-Quality and Motion Consistent Videos using Discrete DiffusionCode0
MDiff-FMT: Morphology-aware Diffusion Model for Fluorescence Molecular Tomography with Small-scale Datasets—0
Decouple-Then-Merge: Towards Better Training for Diffusion Models—0
MaskBlur: Spatial and Angular Data Augmentation for Light Field Image Super-ResolutionCode0
NetDiff: Deep Graph Denoising Diffusion for Ad Hoc Network Topology Generation—0
IncSAR: A Dual Fusion Incremental Learning Framework for SAR Target RecognitionCode0
PostCast: Generalizable Postprocessing for Precipitation Nowcasting via Unsupervised Blurriness Modeling—0
A noise-corrected Langevin algorithm and sampling by half-denoising—0
Brain Mapping with Dense Features: Grounding Cortical Semantic Selectivity in Natural Images With Vision TransformersCode0
Bi-Directional MS Lesion Filling and Synthesis Using Denoising Diffusion Implicit Model-based Lesion Repainting—0
Revealing Directions for Text-guided 3D Face Editing—0
HE-Drive: Human-Like End-to-End Driving with Vision Language Models—0
BrainCodec: Neural fMRI codec for the decoding of cognitive brain statesCode0
VideoGuide: Improving Video Diffusion Models without Training Through a Teacher's Guide—0
Multiscale Latent Diffusion Model for Enhanced Feature Extraction from Medical Images—0
From Incomplete Coarse-Grained to Complete Fine-Grained: A Two-Stage Framework for Spatiotemporal Data Reconstruction—0
Epsilon-VAE: Denoising as Visual Decoding—0
Consistent Autoformalization for Constructing Mathematical LibrariesCode0
Structure-Enhanced Protein Instruction Tuning: Towards General-Purpose Protein Understanding with LLMs—0
Aircraft Radar Altimeter Interference Mitigation Through a CNN-Layer Only Denoising Autoencoder Architecture—0
VEDIT: Latent Prediction Architecture For Procedural Video Representation Learning—0
Generative Edge Detection with Stable Diffusion—0
Classification-Denoising Networks—0
MDSGen: Fast and Efficient Masked Diffusion Temporal-Aware Transformers for Open-Domain Sound Generation—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