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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 6101–6125 of 7282 papers

TitleStatusHype
Uncover the Ground-Truth Relations in Distant Supervision: A Neural Expectation-Maximization FrameworkCode0
Rethinking the CSC Model for Natural ImagesCode0
NODE: Extreme Low Light Raw Image Denoising using a Noise Decomposition Network—0
Sparse Recovery and Dictionary Learning from Nonlinear Compressive Measurements—0
Fast And Efficient Boolean Matrix Factorization By Geometric Segmentation—0
Image denoising using deep CNN with batch renormalizationCode0
Semantic Correlation Promoted Shape-Variant Context for SegmentationCode0
Online Regularization by Denoising with Applications to Phase Retrieval—0
Beyond Photo Realism for Domain Adaptation from Synthetic Data—0
Denoising Auto-encoding Priors in Undecimated Wavelet Domain for MR Image ReconstructionCode1
Self-Supervised Deep Depth DenoisingCode0
Learned Semantic Multi-Sensor Depth Map Fusion—0
Credit Card Fraud Detection Using Autoencoder Neural NetworkCode0
Variational Denoising Network: Toward Blind Noise Modeling and RemovalCode1
Learning a Multi-Domain Curriculum for Neural Machine Translation—0
Complex Deep Learning Models for Denoising of Human Heart ECG signals—0
Automatic Detection of ECG Abnormalities by using an Ensemble of Deep Residual Networks with Attention—0
Nearest Neighbor Search-Based Bitwise Source Separation Using Discriminant Winner-Take-All Hashing—0
Deep Camera: A Fully Convolutional Neural Network for Image Signal Processing—0
Denoising based Sequence-to-Sequence Pre-training for Text Generation—0
Signal denoising based on the Schr\"odinger operator's eigenspectrum and a curvature constraint—0
Denoising and Verification Cross-Layer Ensemble Against Black-box Adversarial Attacks—0
Spatio-temporal Manifold Learning for Human Motions via Long-horizon Modeling—0
A Blind Multiscale Spatial Regularization Framework for Kernel-based Spectral Unmixing—0
Convolutional Neural Network with Median Layers for Denoising Salt-and-Pepper ContaminationsCode0
Show:102550
← PrevPage 245 of 292Next →
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