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
FFTLasso: Large-Scale LASSO in the Fourier Domain—0
Efficient Manifold and Subspace Approximations with SphereletsCode0
Class-specific image denoising using importance sampling—0
When Image Denoising Meets High-Level Vision Tasks: A Deep Learning ApproachCode0
Learning and Evaluating Musical Features with Deep Autoencoders—0
A Bayesian Hyperprior Approach for Joint Image Denoising and Interpolation, with an Application to HDR Imaging—0
Class-specific Poisson denoising by patch-based importance sampling—0
Gated Orthogonal Recurrent Units: On Learning to ForgetCode0
Multi-channel Weighted Nuclear Norm Minimization for Real Color Image Denoising—0
Global hard thresholding algorithms for joint sparse image representation and denoising—0
Jeffrey's prior sampling of deep sigmoidal networks—0
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
Self-Committee Approach for Image Restoration Problems using Convolutional Neural Network—0
External Prior Guided Internal Prior Learning for Real-World Noisy Image Denoising—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
A Design Methodology for Efficient Implementation of Deconvolutional Neural Networks on an FPGACode0
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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