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 4776–4800 of 7282 papers

TitleStatusHype
Reconstruction of Hidden Representation for Robust Feature Extraction—0
Reconstruction of Sound Field through Diffusion Models—0
ReconXF: Graph Reconstruction Attack via Public Feature Explanations on Privatized Node Features and Labels—0
ReCoRe: Regularized Contrastive Representation Learning of World Model—0
Unrolling Plug-and-Play Gradient Graph Laplacian Regularizer for Image Restoration—0
Recovering implicit pitch contours from formants in whispered speech—0
Recovering Loss to Followup Information Using Denoising Autoencoders—0
Recovering Pulse Waves from Video Using Deep Unrolling and Deep Equilibrium Models—0
Recovery of surfaces and functions in high dimensions: sampling theory and links to neural networks—0
Rectified Diffusion Guidance for Conditional Generation—0
Rectifier Neural Network with a Dual-Pathway Architecture for Image Denoising—0
Recurrent Deep Kernel Learning of Dynamical Systems—0
Recurrent Generative Adversarial Networks for Proximal Learning and Automated Compressive Image Recovery—0
Discovery and Expansion of New Domains within Diffusion Models—0
Windowed total variation denoising and noise variance monitoring—0
Recursive Filter for Space-Variant Variance Reduction—0
ReDiffDet: Rotation-equivariant Diffusion Model for Oriented Object Detection—0
ReDistill: Residual Encoded Distillation for Peak Memory Reduction—0
Reduced Effectiveness of Kolmogorov-Arnold Networks on Functions with Noise—0
Reducing Redundancy in the Bottleneck Representation of the Autoencoders—0
Reduction of Non-stationary Noise for a Robotic Living Assistant using Sparse Non-negative Matrix Factorization—0
RefiDiff: Refinement-Aware Diffusion for Efficient Missing Data Imputation—0
Refined Risk Bounds for Unbounded Losses via Transductive Priors—0
RefineVIS: Video Instance Segmentation with Temporal Attention Refinement—0
Zero-Shot Metric Depth with a Field-of-View Conditioned Diffusion Model—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