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 2201–2225 of 7282 papers

TitleStatusHype
Dehazing Ultrasound using Diffusion Models—0
Behind the Noise: Conformal Quantile Regression Reveals Emergent Representations—0
Degradation-Noise-Aware Deep Unfolding Transformer for Hyperspectral Image Denoising—0
BEFD: Boundary Enhancement and Feature Denoising for Vessel Segmentation—0
A Machine Learning Approach for Denoising and Upsampling HRTFs—0
Efficient Denoising Method to Improve The Resolution of Satellite Images—0
DeGLIF for Label Noise Robust Node Classification using GNNs—0
Be Decisive: Noise-Induced Layouts for Multi-Subject Generation—0
Beamspace Channel Estimation for Wideband Millimeter-Wave MIMO: A Model-Driven Unsupervised Learning Approach—0
Alternating Phase Langevin Sampling with Implicit Denoiser Priors for Phase Retrieval—0
Deformation corrected compressed sensing (DC-CS): a novel framework for accelerated dynamic MRI—0
Beam-Shape Effects and Noise Removal from THz Time-Domain Images in Reflection Geometry in the 0.25-6 THz Range—0
Defense against adversarial attacks on deep convolutional neural networks through nonlocal denoising—0
Alternating minimization for a single step TV-Stokes model for image denoising—0
Adaptive Image Denoising by Mixture Adaptation—0
EEG-Based Speech Decoding: A Novel Approach Using Multi-Kernel Ensemble Diffusion Models—0
Defending Spiking Neural Networks against Adversarial Attacks through Image Purification—0
BCDDM: Branch-Corrected Denoising Diffusion Model for Black Hole Image Generation—0
Accelerate High-Quality Diffusion Models with Inner Loop Feedback—0
Defending Against Adversarial Iris Examples Using Wavelet Decomposition—0
Bayes-Optimal Unsupervised Learning for Channel Estimation in Near-Field Holographic MIMO—0
Alternating Iteratively Reweighted Minimization Algorithms for Low-Rank Matrix Factorization—0
3D Conditional Generative Adversarial Networks to enable large-scale seismic image enhancement—0
Deep Variation Prior: Joint Image Denoising and Noise Variance Estimation without Clean Data—0
Deep unsupervised 3D human body reconstruction from a sparse set of landmarks—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