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 1701–1725 of 7282 papers

TitleStatusHype
Non-local Meets Global: An Integrated Paradigm for Hyperspectral DenoisingCode1
Noise2Void - Learning Denoising from Single Noisy ImagesCode1
Unprocessing Images for Learned Raw DenoisingCode1
Deep Recurrent Neural Networks for ECG Signal DenoisingCode1
Fully Convolutional Pixel Adaptive Image DenoiserCode1
Toward Convolutional Blind Denoising of Real PhotographsCode1
From Rank Estimation to Rank Approximation: Rank Residual Constraint for Image RestorationCode1
Speech Denoising with Deep Feature LossesCode1
Deep Energy Estimator NetworksCode1
k-Space Deep Learning for Accelerated MRICode1
Learning to See in the DarkCode1
Noise2Noise: Learning Image Restoration without Clean DataCode1
Deep Image PriorCode1
Video Enhancement with Task-Oriented FlowCode1
Image Restoration by Iterative Denoising and Backward ProjectionsCode1
Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual LossCode1
Recurrent Inference Machines for Solving Inverse ProblemsCode1
MIDA: Multiple Imputation using Denoising AutoencodersCode1
Denoising Adversarial AutoencodersCode1
Deep Convolutional Denoising of Low-Light ImagesCode1
On-Demand Learning for Deep Image RestorationCode1
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image DenoisingCode1
Iterative Gaussianization: from ICA to Random RotationsCode1
Efficient Sum of Outer Products Dictionary Learning (SOUP-DIL) and Its Application to Inverse ProblemsCode1
Domain Generalization for Object Recognition with Multi-task AutoencodersCode1
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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