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 4051–4075 of 7282 papers

TitleStatusHype
Multi-Scale Weighted Nuclear Norm Image Restoration—0
Multi-Slice Fusion for Sparse-View and Limited-Angle 4D CT Reconstruction—0
Multispectral image denoising with optimized vector non-local mean filter—0
Multispectral Images Denoising by Intrinsic Tensor Sparsity Regularization—0
Multispectral Remote Sensing for Weed Detection in West Australian Agricultural Lands—0
Multistable Shape from Shading Emerges from Patch Diffusion—0
Multi-stage Deep Learning Artifact Reduction for Pallel-beam Computed Tomography—0
Train Hard, Finetune Easy: Multilingual Denoising for RDF-to-Text Generation—0
Training a Task-Specific Image Reconstruction Loss—0
Multi-Step Guided Diffusion for Image Restoration on Edge Devices: Toward Lightweight Perception in Embodied AI—0
Training Auto-encoders Effectively via Eliminating Task-irrelevant Input Variables—0
Training Data Size Induced Double Descent For Denoising Neural Networks and the Role of Training Noise Level—0
DiffAug: A Diffuse-and-Denoise Augmentation for Training Robust Classifiers—0
Multi-task Learning for Multilingual Neural Machine Translation—0
Multitask Learning for SAR Ship Detection with Gaussian-Mask Joint Segmentation—0
Multi-task Learning To Improve Semantic Segmentation Of CBCT Scans Using Image Reconstruction—0
Multitemporal SAR images change detection and visualization using RABASAR and simplified GLR—0
Multi-Track Timeline Control for Text-Driven 3D Human Motion Generation—0
Multivariate extensions of isotonic regression and total variation denoising via entire monotonicity and Hardy-Krause variation—0
Multivariate Medians for Image and Shape Analysis—0
Multivariate Signal Denoising Based on Generic Multivariate Detrended Fluctuation Analysis—0
Training Diffusion Models with Federated Learning—0
Multiview Hessian Discriminative Sparse Coding for Image Annotation—0
A Fourier Space Perspective on Diffusion Models—0
Multi-View Networks for Denoising of Arbitrary Numbers of Channels—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