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 4376–4400 of 7282 papers

TitleStatusHype
Stochastic Primal-Dual Three Operator Splitting Algorithm with Extension to Equivariant Regularization-by-Denoising—0
A Modified PINN Approach for Identifiable Compartmental Models in Epidemiology with Applications to COVID-19—0
A Survey on Masked Autoencoder for Self-supervised Learning in Vision and Beyond—0
Deep learning-based denoising for fast time-resolved flame emission spectroscopy in high-pressure combustion environmentCode1
Restoring Vision in Adverse Weather Conditions with Patch-Based Denoising Diffusion ModelsCode2
DnSwin: Toward Real-World Denoising via Continuous Wavelet Sliding-TransformerCode1
Real Image Restoration via Structure-preserving Complementarity Attention—0
Constrained and unconstrained deep image prior optimization models with automatic regularizationCode0
Pose-NDF: Modeling Human Pose Manifolds with Neural Distance FieldsCode2
Edge-Aware Autoencoder Design for Real-Time Mixture-of-Experts Image Compression—0
A Novel ECG Denoising Scheme Using the Ensemble Kalman Filter—0
Image Denoising Using Convolutional Autoencoder—0
Optimizing Image Compression via Joint Learning with DenoisingCode1
Graph Spatio-Spectral Total Variation Model for Hyperspectral Image Denoising—0
Deep Diffusion Models for Seismic Processing—0
Gradient-based Point Cloud Denoising with Uniformity—0
Deep Learning of Radiative Atmospheric Transfer with an AutoencoderCode0
Deep Audio Waveform PriorCode1
Non-Uniform Diffusion Models—0
Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMMCode0
Deep Semantic Statistics Matching (D2SM) Denoising NetworkCode1
Learning Sparsity-Promoting Regularizers using Bilevel Optimization—0
Threat Model-Agnostic Adversarial Defense using Diffusion ModelsCode1
An Overview of Distant Supervision for Relation Extraction with a Focus on Denoising and Pre-training Methods—0
CharFormer: A Glyph Fusion based Attentive Framework for High-precision Character 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