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 5126–5150 of 7282 papers

TitleStatusHype
Self-supervised conformal prediction for uncertainty quantification in Poisson imaging problems—0
Multi-weather Cross-view Geo-localization Using Denoising Diffusion Models—0
Self-supervised denoising for massive noisy images—0
Self-supervised denoising of visual field data improves detection of glaucoma progression—0
Self-supervised Denoising via Diffeomorphic Template Estimation: Application to Optical Coherence Tomography—0
Enhancing convolutional neural network generalizability via low-rank weight approximation—0
Self-supervised Depth Denoising Using Lower- and Higher-quality RGB-D sensors—0
Using Intermediate Forward Iterates for Intermediate Generator Optimization—0
Self-supervised Dynamic CT Perfusion Image Denoising with Deep Neural Networks—0
Self-Supervised Elimination of Non-Independent Noise in Hyperspectral Imaging—0
CLASH: Contrastive learning through alignment shifting to extract stimulus information from EEG—0
Self-Supervised Fast Adaptation for Denoising via Meta-Learning—0
Self-Supervised Inference in State-Space Models—0
Self-supervised Hyperspectral Image Restoration using Separable Image Prior—0
Self-Supervised Image Denoising for Real-World Images with Context-aware Transformer—0
Self-supervised Image Denoising with Downsampled Invariance Loss and Conditional Blind-Spot Network—0
Using Neural Networks for Data Cleaning in Weather Datasets—0
Self-Supervised Learning based CT Denoising using Pseudo-CT Image Pairs—0
Self-supervised learning for crystal property prediction via denoising—0
Using Ornstein-Uhlenbeck Process to understand Denoising Diffusion Probabilistic Model and its Noise Schedules—0
Self-Supervised Learning with Generative Adversarial Networks for Electron Microscopy—0
Self Supervised Low Dose Computed Tomography Image Denoising Using Invertible Network Exploiting Inter Slice Congruence—0
U-Sketch: An Efficient Approach for Sketch to Image Diffusion Models—0
UT5: Pretraining Non autoregressive T5 with unrolled denoising—0
Utilising Low Complexity CNNs to Lift Non-Local Redundancies in Video Coding—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