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 6476–6500 of 7282 papers

TitleStatusHype
A Machine Learning Approach for Denoising and Upsampling HRTFs—0
Diffusion-Based Hierarchical Graph Neural Networks for Simulating Nonlinear Solid Mechanics—0
Video Restoration with a Deep Plug-and-Play Prior—0
Diffusion-Based Image-to-Image Translation by Noise Correction via Prompt Interpolation—0
Diffusion-based Image Translation with Label Guidance for Domain Adaptive Semantic Segmentation—0
Diffusion-based Method for Satellite Pattern-of-Life Identification—0
Diffusion based multi-domain neuroimaging harmonization method with preservation of anatomical details—0
Steered Mixture-of-Experts Autoencoder Design for Real-Time Image Modelling and Denoising—0
Diffusion-based Signal Refiner for Speech Separation—0
Diffusion-Based Signed Distance Fields for 3D Shape Generation—0
Diffusion-Based Symbolic Regression—0
Alternating Phase Langevin Sampling with Implicit Denoiser Priors for Phase Retrieval—0
Alternating minimization for a single step TV-Stokes model for image denoising—0
DiffusionBlocks: Blockwise Training for Generative Models via Score-Based Diffusion—0
Diffusion Boosted Trees—0
Alternating Iteratively Reweighted Minimization Algorithms for Low-Rank Matrix Factorization—0
Steered Mixture of Experts Regression for Image Denoising with Multi-Model-Inference—0
Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction—0
Certified Robustness to Clean-Label Poisoning Using Diffusion Denoising—0
Diffusion Denoising for Low-Dose-CT Model—0
-VAEs : Optimising variational inference by learning data-dependent divergence skew—0
A Low-Cost & Real-Time Motion Capture System—0
DiffusionDialog: A Diffusion Model for Diverse Dialog Generation with Latent Space—0
Diffusion-Driven Domain Adaptation for Generating 3D Molecules—0
Diffusion-Driven Semantic Communication for Generative Models with Bandwidth Constraints—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