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 7226–7250 of 7282 papers

TitleStatusHype
Learning Entity Representation for Entity Disambiguation—0
Extracting Information-rich Part of Texts using Text Denoising—0
Processing stationary noise: model and parameter selection in variational methods—0
Multiview Hessian Discriminative Sparse Coding for Image Annotation—0
OptShrink: An algorithm for improved low-rank signal matrix denoising by optimal, data-driven singular value shrinkage—0
P-HGRMS: A Parallel Hypergraph Based Root Mean Square Algorithm for Image Denoising—0
Optimization of Clustering for Clustering-based Image Denoising—0
Statistical Denoising for single molecule fluorescence microscopic images—0
An Iterated L1 Algorithm for Non-smooth Non-convex Optimization in Computer Vision—0
A New Model and Simple Algorithms for Multi-label Mumford-Shah Problems—0
Sparse Subspace Denoising for Image Manifolds—0
Texture Enhanced Image Denoising via Gradient Histogram Preservation—0
Layer Depth Denoising and Completion for Structured-Light RGB-D Cameras—0
Separating Signal from Noise Using Patch Recurrence across Scales—0
Fully-Connected CRFs with Non-Parametric Pairwise Potential—0
Fast Patch-Based Denoising Using Approximated Patch Geodesic Paths—0
Handling Noise in Single Image Deblurring Using Directional Filters—0
Generalized Denoising Auto-Encoders as Generative ModelsCode0
Blockwise SURE Shrinkage for Non-Local Means—0
Speckle Noise Reduction in Medical Ultrasound Images—0
A Convex Functional for Image Denoising based on Patches with Constrained Overlaps and its vectorial application to Low Dose Differential Phase Tomography—0
Wavelet-denoising on hardware devices with Perfect Reconstruction, low latency and adaptive thresholding—0
An improved wavelet-based signal-denoising architecture with less hardware consumption—0
Semi-Optimal Edge Detector based on Simple Standard Deviation with Adjusted Thresholding—0
Color image denoising by chromatic edges based vector valued 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