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 6851–6875 of 7282 papers

TitleStatusHype
DeepSat - A Learning framework for Satellite ImageryCode0
Successfully Applying Lottery Ticket Hypothesis to Diffusion ModelCode0
Robust Compressive Phase Retrieval via Deep Generative PriorsCode0
Towards a unified view of unsupervised non-local methods for image denoising: the NL-Ridge approachCode0
Robust Deep Ensemble Method for Real-world Image DenoisingCode0
Contextual Checkerboard Denoise -- A Novel Neural Network-Based Approach for Classification-Aware OCT Image DenoisingCode0
An AI System for Continuous Knee Osteoarthritis Severity Grading Using Self-Supervised Anomaly Detection with Limited DataCode0
Sufficient conditions for offline reactivation in recurrent neural networksCode0
DeepSign: Deep Learning for Automatic Malware Signature Generation and ClassificationCode0
DuoDiff: Accelerating Diffusion Models with a Dual-Backbone ApproachCode0
Low Frequency Adversarial PerturbationCode0
RDSA: A Robust Deep Graph Clustering Framework via Dual Soft AssignmentCode0
D-VDAMP: Denoising-based Approximate Message Passing for Compressive MRICode0
DVDnet: A Fast Network for Deep Video DenoisingCode0
Where is the answer? Investigating Positional Bias in Language Model Knowledge ExtractionCode0
SummAE: Zero-Shot Abstractive Text Summarization using Length-Agnostic Auto-EncodersCode0
Summary Refinement through DenoisingCode0
DyLex: Incorporating Dynamic Lexicons into BERT for Sequence LabelingCode0
No-Reference Image Quality Assessment in the Spatial DomainCode0
Weighted Graph Structure Learning with Attention Denoising for Node ClassificationCode0
An adaptively inexact first-order method for bilevel optimization with application to hyperparameter learningCode0
Hybrid Noise Removal in Hyperspectral Imagery With a Spatial-Spectral Gradient NetworkCode0
Dynamic Feature Acquisition Using Denoising AutoencodersCode0
Dynamic Importance in Diffusion U-Net for Enhanced Image SynthesisCode0
Trust the Critics: Generatorless and Multipurpose WGANs with Initial Convergence GuaranteesCode0
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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