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
Defending Spiking Neural Networks against Adversarial Attacks through Image Purification—0
Defense against adversarial attacks on deep convolutional neural networks through nonlocal denoising—0
An algorithm for improving Non-Local Means operators via low-rank approximation—0
Deformation corrected compressed sensing (DC-CS): a novel framework for accelerated dynamic MRI—0
Spectral and Temporal Denoising for Differentially Private Optimization—0
Spectral Convergence Rate of Graph Laplacian—0
Spectral Denoising for Microphone Classification—0
DeGLIF for Label Noise Robust Node Classification using GNNs—0
Acceleration-guided Acoustic Signal Denoising Framework Based on Learnable Wavelet Transform Applied to Slab Track Condition Monitoring—0
Dehazing Ultrasound using Diffusion Models—0
DEK-Forecaster: A Novel Deep Learning Model Integrated with EMD-KNN for Traffic Prediction—0
Spectral Domain Spline Graph Filter Bank—0
Del-Net: A Single-Stage Network for Mobile Camera ISP—0
DEL-Ranking: Ranking-Correction Denoising Framework for Elucidating Molecular Affinities in DNA-Encoded Libraries—0
Delta Denoising Score—0
DemiNet: Dependency-Aware Multi-Interest Network with Self-Supervised Graph Learning for Click-Through Rate Prediction—0
DEMIST: A deep-learning-based task-specific denoising approach for myocardial perfusion SPECT—0
Demonstrating Superresolution in Radar Range Estimation Using a Denoising Autoencoder—0
VEDIT: Latent Prediction Architecture For Procedural Video Representation Learning—0
DENOASR: Debiasing ASRs through Selective Denoising—0
An Adaptive Parameter Estimation for Guided Filter based Image Deconvolution—0
Spectral independent component analysis with noise modeling for M/EEG source separation—0
Denoised Labels for Financial Time-Series Data via Self-Supervised Learning—0
Spectral Processing and Optimization of Static and Dynamic 3D Geometries—0
DENOISER: Rethinking the Robustness for Open-Vocabulary Action Recognition—0
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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