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 676–700 of 7282 papers

TitleStatusHype
ToMCAT: Theory-of-Mind for Cooperative Agents in Teams via Multiagent Diffusion Policies—0
Joint multiband deconvolution for Euclid and Vera C. Rubin images—0
Fast, Accurate Manifold Denoising by Tunneling Riemannian Optimization—0
Unsupervised Accelerated MRI Reconstruction via Ground-Truth-Free Flow Matching—0
Mitigating Hallucinations in Diffusion Models through Adaptive Attention Modulation—0
A Fokker-Planck-Based Loss Function that Bridges Dynamics with Density Estimation—0
MTVHunter: Smart Contracts Vulnerability Detection Based on Multi-Teacher Knowledge TranslationCode0
Noise2Score3D:Unsupervised Tweedie's Approach for Point Cloud Denoising—0
DiffKAN-Inpainting: KAN-based Diffusion model for brain tumor inpainting—0
Improved Diffusion-based Generative Model with Better Adversarial RobustnessCode0
AnyTop: Character Animation Diffusion with Any TopologyCode3
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
Med-gte-hybrid: A contextual embedding transformer model for extracting actionable information from clinical texts—0
Lung-DDPM: Semantic Layout-guided Diffusion Models for Thoracic CT Image SynthesisCode1
MAGO-SP: Detection and Correction of Water-Fat Swaps in Magnitude-Only VIBE MRICode2
Reward-Guided Iterative Refinement in Diffusion Models at Test-Time with Applications to Protein and DNA DesignCode1
Textured 3D Regenerative Morphing with 3D Diffusion Prior—0
Denoising, segmentation and volumetric rendering of optical coherence tomography angiography (OCTA) image using deep learning techniques: a review—0
EyeBench: A Call for More Rigorous Evaluation of Retinal Image EnhancementCode0
A Data-Driven Paradigm-Based Image Denoising and Mosaicking Approach for High-Resolution Acoustic Camera—0
Interleaved Gibbs Diffusion for Constrained Generation—0
Generalization error bound for denoising score matching under relaxed manifold assumption—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