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 6751–6775 of 7282 papers

TitleStatusHype
Plug-and-Play Unplugged: Optimization Free Reconstruction using Consensus Equilibrium—0
Efficient and principled score estimation with Nyström kernel exponential familiesCode0
Unrolled Optimization with Deep PriorsCode0
Image Segmentation by Iterative Inference from Conditional Score EstimationCode0
Deep Sparse Coding Using Optimized Linear Expansion of Thresholds—0
Bitwise Operations of Cellular Automaton on Gray-scale Images—0
Segmented and Non-Segmented Stacked Denoising Autoencoder for Hyperspectral Band Reduction—0
Generalized linear models with low rank effects for network data—0
Learning Convex Regularizers for Optimal Bayesian Denoising—0
External Prior Guided Internal Prior Learning for Real-World Noisy Image Denoising—0
Self-Committee Approach for Image Restoration Problems using Convolutional Neural Network—0
A Cascaded Convolutional Neural Network for X-ray Low-dose CT Image Denoising—0
Adaptive Regularization of Some Inverse Problems in Image Analysis—0
MIDA: Multiple Imputation using Denoising AutoencodersCode1
A Design Methodology for Efficient Implementation of Deconvolutional Neural Networks on an FPGACode0
A Time-Vertex Signal Processing Framework—0
Joint Denoising / Compression of Image Contours via Shape Prior and Context Tree—0
A Faster Patch Ordering Method for Image Denoising—0
Denoising Linear Models with Permuted Data—0
A Dual Sparse Decomposition Method for Image Denoising—0
Learned D-AMP: Principled Neural Network based Compressive Image RecoveryCode0
Boosting with Structural Sparsity: A Differential Inclusion Approach—0
Infinite Sparse Structured Factor Analysis—0
Learning Deep CNN Denoiser Prior for Image RestorationCode0
Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging ProblemsCode0
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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