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 3561–3570 of 7282 papers

TitleStatusHype
Energy-Inspired Self-Supervised Pretraining for Vision Models—0
Image Restoration and Reconstruction using Variable Splitting and Class-adapted Image Priors—0
Energy Dissipation with Plug-and-Play Priors—0
ARFlow: Autogressive Flow with Hybrid Linear Attention—0
Image Restoration using Autoencoding Priors—0
Image Restoration Using Conditional Random Fields and Scale Mixtures of Gaussians—0
Adversarial Signal Denoising with Encoder-Decoder Networks—0
A Coordinate Descent Approach to Atomic Norm Denoising—0
Denoising-based UNMT is more robust to word-order divergence than MASS-based UNMT—0
Energy-Based Processes for Exchangeable Data—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