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 6751–6800 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
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 Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging ProblemsCode0
Learning Deep CNN Denoiser Prior for Image RestorationCode0
3D seismic data denoising using two-dimensional sparse coding scheme—0
On the Relation between Color Image Denoising and Classification—0
Non-Convex Weighted Lp Minimization based Group Sparse Representation Framework for Image Denoising—0
Learning a collaborative multiscale dictionary based on robust empirical mode decomposition—0
Block-Matching Convolutional Neural Network for Image Denoising—0
Nonsymbolic Text Representation—0
Online and Stable Learning of Analysis Operators—0
Image Restoration using Autoencoding Priors—0
Efficient Two-Dimensional Sparse Coding Using Tensor-Linear Combination—0
Discriminative Transfer Learning for General Image Restoration—0
Robust Kronecker-Decomposable Component Analysis for Low-Rank ModelingCode0
ASP: Learning to Forget with Adaptive Synaptic Plasticity in Spiking Neural Networks—0
Learning to Generate Samples from Noise through Infusion TrainingCode0
End-to-End Learning for Structured Prediction Energy Networks—0
QuaSI: Quantile Sparse Image Prior for Spatio-Temporal Denoising of Retinal OCT Data—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