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 6151–6175 of 7282 papers

TitleStatusHype
Discrete Object Generation with Reversible Inductive ConstructionCode0
Low-rank matrix completion and denoising under Poisson noise—0
Perturbation theory approach to study the latent space degeneracy of Variational Autoencoders—0
From Group Sparse Coding to Rank Minimization: A Novel Denoising Model for Low-level Image Restoration—0
A Projectional Ansatz to ReconstructionCode0
Convolutional dictionary learning based auto-encoders for natural exponential-family distributionsCode0
Fast and Provable ADMM for Learning with Generative Priors—0
Regularizing linear inverse problems with convolutional neural networks—0
Multi-level Wavelet Convolutional Neural NetworksCode0
Blind Universal Bayesian Image Denoising with Gaussian Noise Level LearningCode0
Feature-Based Image Clustering and Segmentation Using WaveletsCode0
Seismic data denoising and deblending using deep learning—0
Brno Mobile OCR DatasetCode0
Multilingual Unsupervised NMT using Shared Encoder and Language-Specific Decoders—0
MAAM: A Morphology-Aware Alignment Model for Unsupervised Bilingual Lexicon Induction—0
Unsupervised Bilingual Word Embedding Agreement for Unsupervised Neural Machine Translation—0
Self-supervised Hyperspectral Image Restoration using Separable Image Prior—0
Learning to Rank Broad and Narrow Queries in E-Commerce—0
FastDVDnet: Towards Real-Time Deep Video Denoising Without Flow EstimationCode0
Sparse Solutions of a Class of Constrained Optimization Problems—0
Automated characterization of noise distributions in diffusion MRI dataCode0
One Embedding To Do Them All—0
Sparsity-Assisted Signal Denoising and Pattern Recognition in Time-Series DataCode0
Leveraging Text Repetitions and Denoising Autoencoders in OCR Post-correction—0
Complex Signal Denoising and Interference Mitigation for Automotive Radar Using Convolutional Neural NetworksCode0
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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