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 6801–6825 of 7282 papers

TitleStatusHype
Raw Waveform-based Speech Enhancement by Fully Convolutional Networks—0
A Correlative Denoising Autoencoder to Model Social Influence for Top-N Recommender System—0
Group Sparsity Residual Constraint for Image Denoising—0
On architectural choices in deep learning: From network structure to gradient convergence and parameter estimation—0
Image Analysis Using a Dual-Tree M-Band Wavelet Transform—0
Variational Inference using Implicit Distributions—0
Learning Non-local Image Diffusion for Image Denoising—0
CT Image Denoising with Perceptive Deep Neural Networks—0
An Extended Framework for Marginalized Domain AdaptationCode0
Synthesis versus analysis in patch-based image priors—0
Analyzing the Weighted Nuclear Norm Minimization and Nuclear Norm Minimization based on Group Sparse Representation—0
A Deterministic and Generalized Framework for Unsupervised Learning with Restricted Boltzmann Machines—0
Deep Kernelized Autoencoders—0
Scene-adapted plug-and-play algorithm with convergence guarantees—0
Using Complex Wavelet Transform and Bilateral Filtering for Image Denoising—0
A method of limiting performance loss of CNNs in noisy environments—0
Eye-Movement behavior identification for AD diagnosis—0
Side Information in Robust Principal Component Analysis: Algorithms and Applications—0
Density—0
Denoising Hyperspectral Image with Non-i.i.d. Noise Structure—0
A New Method for Removing the Moire' Pattern from Images—0
Pruned non-local means—0
Dirty Pixels: Towards End-to-End Image Processing and PerceptionCode0
Deep Class Aware DenoisingCode0
Image denoising using group sparsity residual and external nonlocal self-similarity prior—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