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 6051–6075 of 7282 papers

TitleStatusHype
Adaptive Rates for Total Variation Image Denoising—0
3D Conditional Generative Adversarial Networks to enable large-scale seismic image enhancement—0
Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural NetworkCode0
A regression algorithm for accelerated lattice QCD that exploits sparse inference on the D-Wave quantum annealer—0
Recursive Filter for Space-Variant Variance Reduction—0
A Modular Deep Learning Pipeline for Galaxy-Scale Strong Gravitational Lens Detection and Modeling—0
Manifold Denoising by Nonlinear Robust Principal Component AnalysisCode0
Zero-Shot Paraphrase Generation with Multilingual Language Models—0
Sparse Coding on Cascaded Residuals—0
Cross-Scale Residual Network for Multiple Tasks:Image Super-resolution, Denoising, and Deblocking—0
FASPell: A Fast, Adaptable, Simple, Powerful Chinese Spell Checker Based On DAE-Decoder ParadigmCode0
Reinforcement-based denoising of distantly supervised NER with partial annotation—0
Contextual Text Denoising with Masked Language Model—0
NL2pSQL: Generating Pseudo-SQL Queries from Under-Specified Natural Language Questions—0
Denoising and Regularization via Exploiting the Structural Bias of Convolutional GeneratorsCode0
Multivariate Medians for Image and Shape Analysis—0
Conditional Denoising of Remote Sensing Imagery Using Cycle-Consistent Deep Generative Models—0
Jointly optimal dereverberation and beamforming—0
Robust and Computationally-Efficient Anomaly Detection using Powers-of-Two Networks—0
Scrambled Translation Problem: A Problem of Denoising UNMT—0
Contextual Text Denoising with Masked Language Models—0
Approximate Bayesian Computation with the Sliced-Wasserstein DistanceCode0
PT-MMD: A Novel Statistical Framework for the Evaluation of Generative Systems—0
On approximating f with neural networks—0
EdgeFool: An Adversarial Image Enhancement FilterCode0
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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