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 3251–3260 of 7282 papers

TitleStatusHype
Entropy stable conservative flux form neural networks—0
Graph Based Sinogram Denoising for Tomographic Reconstructions—0
Convergence rates for pretraining and dropout: Guiding learning parameters using network structure—0
Convergence of the denoising diffusion probabilistic models for general noise schedules—0
ENSURE: A General Approach for Unsupervised Training of Deep Image Reconstruction Algorithms—0
Graph Convolutional Neural Networks for Automated Echocardiography View Recognition: A Holistic Approach—0
Graph Defense Diffusion Model—0
Convergence of score-based generative modeling for general data distributions—0
Autoregressive Score Matching—0
Convergence of gradient based pre-training in Denoising autoencoders—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