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 6651–6675 of 7282 papers

TitleStatusHype
Learning to Bound: A Generative Cramér-Rao BoundCode0
A Theoretically Guaranteed Quaternion Weighted Schatten p-norm Minimization Method for Color Image RestorationCode0
Semantic Adversarial ExamplesCode0
Learning to compress and search visual data in large-scale systemsCode0
Learning to Decouple and Generate Seismic Random Noise via Invertible Neural NetworkCode0
Learning to Denoise Distantly-Labeled Data for Entity TypingCode0
New Techniques for Preserving Global Structure and Denoising with Low Information Loss in Single-Image Super-ResolutionCode0
Learning to Denoise Biomedical Knowledge Graph for Robust Molecular Interaction PredictionCode0
Unsupervised Hyperspectral Stimulated Raman Microscopy Image Enhancement: Denoising and Segmentation via One-Shot Deep LearningCode0
Advancing Unsupervised Low-light Image Enhancement: Noise Estimation, Illumination Interpolation, and Self-RegulationCode0
Transfer Learning from Synthetic to Real-Noise Denoising with Adaptive Instance NormalizationCode0
DDRNet: Depth Map Denoising and Refinement for Consumer Depth Cameras Using Cascaded CNNsCode0
Graph Signal Recovery Using Restricted Boltzmann MachinesCode0
Diffusion Sampling Path Tells More: An Efficient Plug-and-Play Strategy for Sample FilteringCode0
Topological Slepians: Maximally Localized Representations of Signals over Simplicial ComplexesCode0
CoSTI: Consistency Models for (a faster) Spatio-Temporal ImputationCode0
ACT-Diffusion: Efficient Adversarial Consistency Training for One-step Diffusion ModelsCode0
Graph topology inference benchmarks for machine learningCode0
NLH: A Blind Pixel-level Non-local Method for Real-world Image DenoisingCode0
Learning to Generate Samples from Noise through Infusion TrainingCode0
Learning to Kindle the StarlightCode0
Semantic Correlation Promoted Shape-Variant Context for SegmentationCode0
xUnit: Learning a Spatial Activation Function for Efficient Image RestorationCode0
DiffusionTrack: Point Set Diffusion Model for Visual Object TrackingCode0
Grids Often Outperform Implicit Neural RepresentationsCode0
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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