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 2276–2300 of 7282 papers

TitleStatusHype
FlexiDiT: Your Diffusion Transformer Can Easily Generate High-Quality Samples with Less Compute—0
Spectral Analysis of Representational Similarity with Limited Neurons—0
Spatial-Spectral Diffusion Contrastive Representation Network for Hyperspectral Image Classification—0
cMIM: A Contrastive Mutual Information Framework for Unified Generative and Discriminative Representation Learning—0
MFSR: Multi-fractal Feature for Super-resolution Reconstruction with Fine Details Recovery—0
On the Interpolation Effect of Score Smoothing—0
END: Early Noise Dropping for Efficient and Effective Context Denoising—0
A Dual-Purpose Framework for Backdoor Defense and Backdoor Amplification in Diffusion Models—0
Self-supervised conformal prediction for uncertainty quantification in Poisson imaging problems—0
DenoMAE2.0: Improving Denoising Masked Autoencoders by Classifying Local Patches—0
Leveraging Structural Knowledge in Diffusion Models for Source Localization in Data-Limited Graph Scenarios—0
ToMCAT: Theory-of-Mind for Cooperative Agents in Teams via Multiagent Diffusion Policies—0
DiffKAN-Inpainting: KAN-based Diffusion model for brain tumor inpainting—0
Unsupervised Accelerated MRI Reconstruction via Ground-Truth-Free Flow Matching—0
MTVHunter: Smart Contracts Vulnerability Detection Based on Multi-Teacher Knowledge TranslationCode0
Mitigating Hallucinations in Diffusion Models through Adaptive Attention Modulation—0
Improved Diffusion-based Generative Model with Better Adversarial RobustnessCode0
Noise2Score3D:Unsupervised Tweedie's Approach for Point Cloud Denoising—0
A Fokker-Planck-Based Loss Function that Bridges Dynamics with Density Estimation—0
Joint multiband deconvolution for Euclid and Vera C. Rubin images—0
Fast, Accurate Manifold Denoising by Tunneling Riemannian Optimization—0
Rewards-based image analysis in microscopy—0
Deep unrolling for learning optimal spatially varying regularisation parameters for Total Generalised Variation—0
Generative diffusion for perceptron problems: statistical physics analysis and efficient algorithms—0
DualNeRF: Text-Driven 3D Scene Editing via Dual-Field Representation—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