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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3551–3575 of 7282 papers

TitleStatusHype
Image Generation with Multimodal Priors using Denoising Diffusion Probabilistic Models—0
Image Inpainting Using Directional Tensor Product Complex Tight Framelets—0
Deep learning denoising for EOG artifacts removal from EEG signals—0
Graph Neural Networks and Differential Equations: A hybrid approach for data assimilation of fluid flows—0
Deep learning denoiser assisted roughness measurements extraction from thin resists with low Signal-to-Noise Ratio(SNR) SEM images: analysis with SMILE—0
Auxiliary Features-Guided Super Resolution for Monte Carlo Rendering—0
Graph Laplacian Regularization for Image Denoising: Analysis in the Continuous Domain—0
Graphics2RAW: Mapping Computer Graphics Images to Sensor RAW Images—0
Graph Generation via Spectral Diffusion—0
Deep Learning CT Image Restoration using System Blur and Noise Models—0
Auto Tensor Singular Value Thresholding: A Non-Iterative and Rank-Free Framework for Tensor Denoising—0
Align-A-Video: Deterministic Reward Tuning of Image Diffusion Models for Consistent Video Editing—0
Adaptive Direction-Guided Structure Tensor Total Variation—0
Accelerated first-order primal-dual proximal methods for linearly constrained composite convex programming—0
Image Restoration using Autoencoding Priors—0
Image Restoration Using Conditional Random Fields and Scale Mixtures of Gaussians—0
Graph filtering over expanding graphs—0
Graph Feature Gating Networks—0
Deep Learning-based Symbolic Indoor Positioning using the Serving eNodeB—0
Graph Differentiable Architecture Search with Structure Learning—0
Autoregressive Score Matching—0
Graph Defense Diffusion Model—0
Deep learning-based statistical noise reduction for multidimensional spectral data—0
Graph Convolutional Neural Networks for Automated Echocardiography View Recognition: A Holistic Approach—0
Deep Learning-based Modulation Detection for NOMA Systems—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