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

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Showing 1991–2000 of 7282 papers

TitleStatusHype
Denoising Functional Maps: Diffusion Models for Shape Correspondence—0
DICE: Diverse Diffusion Model with Scoring for Trajectory Prediction—0
Dictionary Learning and Sparse Coding-based Denoising for High-Resolution Task Functional Connectivity MRI Analysis—0
Dictionary Learning Based on Sparse Distribution Tomography—0
Dictionary Learning Under Generative Coefficient Priors with Applications to Compression—0
Dictionary Learning with Equiprobable Matching Pursuit—0
BLADE: Filter Learning for General Purpose Computational Photography—0
Diff-2-in-1: Bridging Generation and Dense Perception with Diffusion Models—0
Denoising Fisher Training For Neural Implicit Samplers—0
Denoising Fast X-Ray Fluorescence Raster Scans of Paintings—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