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 3951–3975 of 7282 papers

TitleStatusHype
Recent progress in image denoising: A training strategy perspective—0
Impact of PCA-based preprocessing and different CNN structures on deformable registration of sonograms—0
Regular Time-series Generation using SGM—0
Dif-Fusion: Towards High Color Fidelity in Infrared and Visible Image Fusion with Diffusion Models—0
Fast Inference in Denoising Diffusion Models via MMD FinetuningCode1
The role of noise in denoising models for anomaly detection in medical imagesCode1
Representing Noisy Image Without DenoisingCode1
Denoising Diffusion Probabilistic Models as a Defense against Adversarial AttacksCode0
Online Filtering over Expanding Graphs—0
FEUNet: a flexible and effective U-shaped network for image denoisingCode0
Diffusion-based Generation, Optimization, and Planning in 3D ScenesCode2
Deep Diversity-Enhanced Feature Representation of Hyperspectral ImagesCode1
NCP: Neural Correspondence Prior for Effective Unsupervised Shape MatchingCode0
DINF: Dynamic Instance Noise Filter for Occluded Pedestrian Detection—0
Thompson Sampling with Diffusion Generative Prior—0
A Possible Converter to Denoise the Images of Exoplanet Candidates through Machine Learning Techniques—0
AdaPoinTr: Diverse Point Cloud Completion with Adaptive Geometry-Aware TransformersCode2
Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language ModelsCode1
Speech Driven Video Editing via an Audio-Conditioned Diffusion Model—0
Tensor Denoising via Amplification and Stable Rank Methods—0
DiffTalk: Crafting Diffusion Models for Generalized Audio-Driven Portraits AnimationCode2
Modiff: Action-Conditioned 3D Motion Generation with Denoising Diffusion Probabilistic Models—0
CLASH: Contrastive learning through alignment shifting to extract stimulus information from EEG—0
Image Denoising: The Deep Learning Revolution and Beyond -- A Survey Paper --—0
Generative Time Series Forecasting with Diffusion, Denoise, and DisentanglementCode2
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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