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 551–575 of 7282 papers

TitleStatusHype
Optimal Denoising in Score-Based Generative Models: The Role of Data Regularity—0
Interpretable Unsupervised Joint Denoising and Enhancement for Real-World low-light ScenariosCode1
PANDORA: Diffusion Policy Learning for Dexterous Robotic Piano Playing—0
Anatomically and Metabolically Informed Diffusion for Unified Denoising and Segmentation in Low-Count PET Imaging—0
From Head to Tail: Towards Balanced Representation in Large Vision-Language Models through Adaptive Data Calibration—0
A Design of Denser-Graph-Frequency Graph Fourier Frames for Graph Signal Analysis—0
Zero-Shot Denoising for Fluorescence Lifetime Imaging Microscopy with Intensity-Guided LearningCode0
State Fourier Diffusion Language Model (SFDLM): A Scalable, Novel Iterative Approach to Language Modeling—0
Personalize Anything for Free with Diffusion Transformer—0
Weighted Graph Structure Learning with Attention Denoising for Node ClassificationCode0
DiffGAP: A Lightweight Diffusion Module in Contrastive Space for Bridging Cross-Model Gap—0
DriveGEN: Generalized and Robust 3D Detection in Driving via Controllable Text-to-Image Diffusion GenerationCode1
Zero-TIG: Temporal Consistency-Aware Zero-Shot Illumination-Guided Low-light Video EnhancementCode0
Towards Better Alignment: Training Diffusion Models with Reinforcement Learning Against Sparse RewardsCode2
PSF-4D: A Progressive Sampling Framework for View Consistent 4D Editing—0
Advancing 3D Gaussian Splatting Editing with Complementary and Consensus Information—0
Watch and Learn: Leveraging Expert Knowledge and Language for Surgical Video Understanding—0
Noise Synthesis for Low-Light Image Denoising with Diffusion Models—0
Are Deep Speech Denoising Models Robust to Adversarial Noise?—0
From Score Matching to Diffusion: A Fine-Grained Error Analysis in the Gaussian Setting—0
Sparse Dictionary Learning for Image Recovery by Iterative Shrinkage—0
FlowTok: Flowing Seamlessly Across Text and Image TokensCode5
CoStoDet-DDPM: Collaborative Training of Stochastic and Deterministic Models Improves Surgical Workflow Anticipation and RecognitionCode0
ROODI: Reconstructing Occluded Objects with Denoising Inpainters—0
Studying Classifier(-Free) Guidance From a Classifier-Centric Perspective—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