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 4201–4225 of 7282 papers

TitleStatusHype
Non-Contact Heart Rate Measurement from Deteriorated Videos—0
Nonconvex Approach for Sparse and Low-Rank Constrained Models with Dual Momentum—0
T-SVD Based Non-convex Tensor Completion and Robust Principal Component Analysis—0
Non-Convex Weighted Lp Minimization based Group Sparse Representation Framework for Image Denoising—0
Non-Denoising Forward-Time Diffusions—0
DDAE++: Enhancing Diffusion Models Towards Unified Generative and Discriminative Learning—0
Nonlinear Denoising, Linear Demixing—0
Nonlinear denoising score matching for enhanced learning of structured distributions—0
Nonlinear Filter Based Image Denoising Using AMF Approach—0
Nonlinear Inverse Design of Mechanical Multi-Material Metamaterials Enabled by Video Denoising Diffusion and Structure Identifier—0
Non-linear prediction with LSTM recurrent neural networks for acoustic novelty detection—0
Non-line-of-sight reconstruction via structure sparsity regularization—0
Non-Local Color Image Denoising with Convolutional Neural Networks—0
TruePose: Human-Parsing-guided Attention Diffusion for Full-ID Preserving Pose Transfer—0
Truncated Consistency Models—0
Non-Local Part-Aware Point Cloud Denoising—0
Non-Parametric Bayesian Dictionary Learning for Sparse Image Representations—0
Nonperfused Retinal Capillaries -- A New Method Developed on OCT and OCTA—0
Non-rigid Motion Correction for MRI Reconstruction via Coarse-To-Fine Diffusion Models—0
Adaptive Denoising via GainTuning—0
Nonsymbolic Text Representation—0
Nonsymbolic Text Representation—0
Non-Uniform Diffusion Models—0
Non-Uniform Exposure Imaging via Neuromorphic Shutter Control—0
A Correlative Denoising Autoencoder to Model Social Influence for Top-N Recommender System—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