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

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Showing 5301–5325 of 7282 papers

TitleStatusHype
A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis—0
V2X-R: Cooperative LiDAR-4D Radar Fusion with Denoising Diffusion for 3D Object Detection—0
Validation and Generalizability of Self-Supervised Image Reconstruction Methods for Undersampled MRI—0
Are Deep Speech Denoising Models Robust to Adversarial Noise?—0
SIMBA: Scalable Inversion in Optical Tomography using Deep Denoising Priors—0
A Refined 3D Gaussian Representation for High-Quality Dynamic Scene Reconstruction—0
A Refined Inertial DC Algorithm for DC Programming—0
A regression algorithm for accelerated lattice QCD that exploits sparse inference on the D-Wave quantum annealer—0
A Regularization Approach to Blind Deblurring and Denoising of QR Barcodes—0
Are Large-scale Datasets Necessary for Self-Supervised Pre-training?—0
A Research and Strategy of Remote Sensing Image Denoising Algorithms—0
A Residual Solver and Its Unfolding Neural Network for Total Variation Regularized Models—0
A Review of an Old Dilemma: Demosaicking First, or Denoising First?—0
A review of mean-shift algorithms for clustering—0
A Review of Multi-Objective Deep Learning Speech Denoising Methods—0
InSAR Phase Denoising: A Review of Current Technologies and Future Directions—0
A Review Paper: Noise Models in Digital Image Processing—0
Similarity-Guided Diffusion for Contrastive Sequential Recommendation—0
ARFlow: Autogressive Flow with Hybrid Linear Attention—0
A Brief Survey of Recent Edge-Preserving Smoothing Algorithms on Digital Images—0
A Robust Completed Local Binary Pattern (RCLBP) for Surface Defect Detection—0
A New Model and Simple Algorithms for Multi-label Mumford-Shah Problems—0
Array SAR 3D Sparse Imaging Based on Regularization by Denoising Under Few Observed Data—0
ART: Artifact Removal Transformer for Reconstructing Noise-Free Multichannel Electroencephalographic Signals—0
Artefact removal in ground truth deficient fluctuations-based nanoscopy images using deep learning—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