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 6901–6925 of 7282 papers

TitleStatusHype
Bayesian selection for the l2-Potts model regularization parameter: 1D piecewise constant signal denoising—0
A Non-Local Conventional Approach for Noise Removal in 3D MRI—0
Parameter Learning for Log-supermodular Distributions—0
Medical image denoising using convolutional denoising autoencodersCode0
A Comparative Study for the Nuclear Norms Minimization Methods—0
Fractional Calculus In Image Processing: A Review—0
Online Adaptation of Deep Architectures with Reinforcement Learning—0
Sparse Filtered SIRT for Electron Tomography—0
Transductive Adaptation of Black Box Predictions—0
A Domain Adaptation Regularization for Denoising Autoencoders—0
Neural shrinkage for wavelet-based SAR despeckling—0
Denoising and compression in wavelet domain via projection onto approximation coefficients—0
Denoising based on wavelets and deblurring via self-organizing map for Synthetic Aperture Radar images—0
Online Nonnegative Matrix Factorization with General Divergences—0
A Nonlocal Denoising Algorithm for Manifold-Valued Images Using Second Order Statistics—0
Generic 3D Convolutional Fusion for image restoration—0
Mesh Denoising based on Normal Voting Tensor and Binary Optimization—0
A Haar Wavelet-Based Perceptual Similarity Index for Image Quality Assessment—0
Optimal Rates of Statistical Seriation—0
A Permutation-based Model for Crowd Labeling: Optimal Estimation and Robustness—0
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip ConnectionsCode0
Accelerated first-order primal-dual proximal methods for linearly constrained composite convex programming—0
Non Local Spatial and Angular Matching : Enabling higher spatial resolution diffusion MRI datasets through adaptive denoisingCode0
Fast Multi-Layer Laplacian Enhancement—0
Towards stationary time-vertex signal processing—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