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 5226–5250 of 7282 papers

TitleStatusHype
Side Information in Robust Principal Component Analysis: Algorithms and Applications—0
Ankh3: Multi-Task Pretraining with Sequence Denoising and Completion Enhances Protein Representations—0
Annealed Denoising score matching: learning Energy based model in high-dimensional spaces—0
An Expectation-Maximization Algorithm for Training Clean Diffusion Models from Corrupted Observations—0
A noise-corrected Langevin algorithm and sampling by half-denoising—0
A Noise is Worth Diffusion Guidance—0
A Noise-level-aware Framework for PET Image Denoising—0
A noise reduction method for force measurements in water entry experiments based on the Ensemble Empirical Mode Decomposition—0
Anomaly detection using Diffusion-based methods—0
Sigma Flows for Image and Data Labeling and Learning Structured Prediction—0
A New Recurrent Plug-and-Play Prior Based on the Multiple Self-Similarity Network—0
A Non-Asymptotic Analysis of Oversmoothing in Graph Neural Networks—0
A Non-Local Conventional Approach for Noise Removal in 3D MRI—0
A Nonlocal Denoising Algorithm for Manifold-Valued Images Using Second Order Statistics—0
A Nonlocal InSAR Filter for High-Resolution DEM Generation from TanDEM-X Interferograms—0
A Non-Local Means Approach for Gaussian Noise Removal from Images using a Modified Weighting Kernel—0
A Non-Local Means Filter for Removing the Poisson Noise—0
An Operator-Splitting Method for the Gaussian Curvature Regularization Model with Applications to Surface Smoothing and Imaging—0
An Optimal Control Approach to Early Stopping Variational Methods for Image Restoration—0
An Optimal Reduction of TV-Denoising to Adaptive Online Learning—0
An Optimal Statistical and Computational Framework for Generalized Tensor Estimation—0
A Deep Unrolling Model with Hybrid Optimization Structure for Hyperspectral Image Deconvolution—0
Signal denoising based on the Schr\"odinger operator's eigenspectrum and a curvature constraint—0
A note on patch-based low-rank minimization for fast image denoising—0
A Note on the Convergence of Denoising Diffusion Probabilistic Models—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