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

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Showing 4801–4825 of 7282 papers

TitleStatusHype
A comparison study of CNN denoisers on PRNU extraction—0
Label-Efficient Semantic Segmentation with Diffusion ModelsCode1
A Survey on Deep learning based Document Image Enhancement—0
Noise Distribution Adaptive Self-Supervised Image Denoising using Tweedie Distribution and Score Matching—0
Snapshot HDR Video Construction Using Coded Mask—0
Efficient joint noise removal and multi exposure fusion—0
Geometry-aware Two-scale PIFu Representation for Human Reconstruction—0
Global Context with Discrete Diffusion in Vector Quantised Modelling for Image Generation—0
SAR Image Despeckling Using Continuous Attention ModuleCode1
Wide-Sense Stationarity in Generalized Graph Signal Processing—0
Recovering Hölder smooth functions from noisy modulo samplesCode0
CDLNet: Noise-Adaptive Convolutional Dictionary Learning Network for Blind Denoising and DemosaicingCode1
Embedding Decomposition for Artifacts Removal in EEG SignalsCode1
Learning Robust Recommender from Noisy Implicit FeedbackCode1
Total-Body Low-Dose CT Image Denoising using Prior Knowledge Transfer Technique with Contrastive Regularization Mechanism—0
Curriculum Disentangled Recommendation with Noisy Multi-feedbackCode1
Stochastic Solutions for Linear Inverse Problems using the Prior Implicit in a Denoiser—0
It Has Potential: Gradient-Driven Denoisers for Convergent Solutions to Inverse Problems—0
Denoising Normalizing FlowCode1
Noise2Score: Tweedie’s Approach to Self-Supervised Image Denoising without Clean Images—0
Neighborhood Reconstructing AutoencodersCode1
Graph Differentiable Architecture Search with Structure Learning—0
Three-dimensional spike localization and improved motion correction for Neuropixels recordings—0
Revisiting Temporal Alignment for Video RestorationCode1
Assessment of Data Consistency through Cascades of Independently Recurrent Inference Machines for fast and robust accelerated MRI reconstructionCode1
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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