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 2376–2400 of 7282 papers

TitleStatusHype
Cross-Domain Image Conversion by CycleDM—0
On the Asymptotic Mean Square Error Optimality of Diffusion ModelsCode1
OOTDiffusion: Outfitting Fusion based Latent Diffusion for Controllable Virtual Try-onCode9
ViewDiff: 3D-Consistent Image Generation with Text-to-Image ModelsCode3
UB-FineNet: Urban Building Fine-grained Classification Network for Open-access Satellite Images—0
Improving Adversarial Energy-Based Model via Diffusion Process—0
Diffusion-TS: Interpretable Diffusion for General Time Series GenerationCode3
DiffSal: Joint Audio and Video Learning for Diffusion Saliency Prediction—0
Near-Real-Time Mueller Polarimetric Image Processing for Neurosurgical Intervention—0
List-Mode PET Image Reconstruction Using Dykstra-Like Splitting—0
Dual-domain strip attention for image restorationCode2
Point Processes and spatial statistics in time-frequency analysis—0
DiffAssemble: A Unified Graph-Diffusion Model for 2D and 3D ReassemblyCode2
Graph Convolutional Neural Networks for Automated Echocardiography View Recognition: A Holistic Approach—0
TEncDM: Understanding the Properties of the Diffusion Model in the Space of Language Model EncodingsCode1
Listening to the Noise: Blind Denoising with Gibbs DiffusionCode1
Graph Generation via Spectral Diffusion—0
Unified Generation, Reconstruction, and Representation: Generalized Diffusion with Adaptive Latent Encoding-DecodingCode0
CollaFuse: Navigating Limited Resources and Privacy in Collaborative Generative AICode0
ViewFusion: Towards Multi-View Consistency via Interpolated DenoisingCode2
Balancing Act: Distribution-Guided Debiasing in Diffusion Models—0
ROG_PL: Robust Open-Set Graph Learning via Region-Based Prototype Learning—0
Multi-Scale Denoising in the Feature Space for Low-Light Instance Segmentation—0
Self-Supervised Learning with Generative Adversarial Networks for Electron Microscopy—0
Objective and Interpretable Breast Cosmesis Evaluation with Attention Guided Denoising Diffusion Anomaly Detection Model—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