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 4951–4975 of 7282 papers

TitleStatusHype
Robust Hyperspectral Image Fusion with Simultaneous Guide Image Denoising via Constrained Convex Optimization—0
Unsupervised Low-dose CT Reconstruction with One-way Conditional Normalizing Flows—0
Robust Image Filtering Using Joint Static and Dynamic Guidance—0
Accelerating Prostate Diffusion Weighted MRI using Guided Denoising Convolutional Neural Network: Retrospective Feasibility Study—0
Robust Knowledge Graph Embedding via Denoising—0
Robust Kronecker Component Analysis—0
Robust Learning with Adaptive Sample Credibility Modeling—0
Robust Low-Light Human Pose Estimation through Illumination-Texture Modulation—0
Robust Matrix Factorization with Grouping Effect—0
Robust Multimodal Fusion for Human Activity Recognition—0
Robustness Of Saak Transform Against Adversarial Attacks—0
Robust Non-Linear Matrix Factorization for Dictionary Learning, Denoising, and Clustering—0
Robust Non-linear Regression: A Greedy Approach Employing Kernels with Application to Image Denoising—0
Robust non-local means filter for ultrasound image denoising—0
Robust Online Classification: From Estimation to Denoising—0
Robust Piecewise-Constant Smoothing: M-Smoother Revisited—0
Robust plug-and-play methods for highly accelerated non-Cartesian MRI reconstruction—0
Unsupervised Microscopy Video Denoising—0
Robust Principal Component Analysis: A Median of Means Approach—0
Robust Product Classification with Instance-Dependent Noise—0
Robust Regression on Image Manifolds for Ordered Label Denoising—0
Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption—0
Robust semi-supervised segmentation with timestep ensembling diffusion models—0
Robust single-particle cryo-EM image denoising and restoration—0
Unsupervised Monocular Depth Estimation Based on Hierarchical Feature-Guided Diffusion—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