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 7076–7100 of 7282 papers

TitleStatusHype
The Devil's Advocate: Shattering the Illusion of Unexploitable Data using Diffusion ModelsCode0
A note on the evaluation of generative modelsCode0
ImPoster: Text and Frequency Guidance for Subject Driven Action Personalization using Diffusion ModelsCode0
Deep Sparse and Low-Rank Prior for Hyperspectral Image DenoisingCode0
Tuning-Free Visual Customization via View Iterative Self-Attention ControlCode0
Quantization-Based Regularization for AutoencodersCode0
Can Deep Learning Outperform Modern Commercial CT Image Reconstruction Methods?Code0
Improved Diffusion-based Generative Model with Better Adversarial RobustnessCode0
Medical image denoising using convolutional denoising autoencodersCode0
Can denoising diffusion probabilistic models generate realistic astrophysical fields?Code0
Ensembling Diffusion Models via Adaptive Feature AggregationCode0
A Weighted Difference of Anisotropic and Isotropic Total Variation for Relaxed Mumford-Shah Color and Multiphase Image SegmentationCode0
Quantum Annealing for Robust Principal Component AnalysisCode0
SU-YOLO: Spiking Neural Network for Efficient Underwater Object DetectionCode0
Improved Out-of-Scope Intent Classification with Dual Encoding and Threshold-based Re-ClassificationCode0
Decoding Phone Pairs from MEG Signals Across Speech ModalitiesCode0
Megapixel Image Generation with Step-Unrolled Denoising AutoencodersCode0
Equivariant Blurring Diffusion for Hierarchical Molecular Conformer GenerationCode0
Decoding the shift-invariant data: applications for band-excitation scanning probe microscopyCode0
Quantum Diffusion Model for Quark and Gluon Jet GenerationCode0
MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and EnhancementCode0
A distribution-dependent Mumford-Shah model for unsupervised hyperspectral image segmentationCode0
MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Frame Interpolation and EnhancementCode0
DARK: Denoising, Amplification, Restoration KitCode0
The Effect of Optimal Self-Distillation in Noisy Gaussian Mixture ModelCode0
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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