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 6126–6150 of 7282 papers

TitleStatusHype
Multi-Kernel Filtering for Nonstationary Noise: An Extension of Bilateral Filtering Using Image Context—0
A Comparative Study of Filtering Approaches Applied to Color Archival Document Images—0
On Convex Duality in Linear Inverse Problems—0
Fast Sub-millimeter Diffusion MRI using gSlider-SMS and SNR-Enhancing Joint Reconstruction—0
DAPAS : Denoising Autoencoder to Prevent Adversarial attack in Semantic Segmentation—0
PCGAN-CHAR: Progressively Trained Classifier Generative Adversarial Networks for Classification of Noisy Handwritten Bangla Characters—0
Index NetworkCode0
Bias and variance reduction and denoising for CTF Estimation—0
WhiteNNer-Blind Image Denoising via Noise Whiteness Priors—0
Defending Against Adversarial Iris Examples Using Wavelet Decomposition—0
Manifold Modeling in Embedded Space: A Perspective for Interpreting Deep Image PriorCode0
CameraNet: A Two-Stage Framework for Effective Camera ISP Learning—0
Semi-supervised representation learning via dual autoencoders for domain adaptationCode0
Attention Guided Low-light Image Enhancement with a Large Scale Low-light Simulation DatasetCode0
An Underparametrized Deep Decoder Architecture for Graph SignalsCode0
The LMU Munich Unsupervised Machine Translation System for WMT19—0
Pyramid Real Image Denoising NetworkCode0
Few-Shot Meta-Denoising—0
A New Recurrent Plug-and-Play Prior Based on the Multiple Self-Similarity Network—0
Correlation Distance Skip Connection Denoising Autoencoder (CDSK-DAE) for Speech Feature Enhancement—0
Summary Refinement through DenoisingCode0
Feature Graph Learning for 3D Point Cloud Denoising—0
Radio Frequency Fingerprint Identification Based on Denoising Autoencoders—0
An Optimal Control Approach to Early Stopping Variational Methods for Image Restoration—0
Deep Graph-Convolutional Image DenoisingCode0
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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