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
Enhanced channel estimation for near-field IRS-aided multi-user MIMO system via a large deep residual network—0
Image Processing Tools for Financial Time Series Classification—0
Enhanced artificial intelligence-based diagnosis using CBCT with internal denoising: Clinical validation for discrimination of fungal ball, sinusitis, and normal cases in the maxillary sinus—0
A Coupling Enhancement Algorithm for ZrO2 Ceramic Bearing Ball Surface Defect Detection Based on Cartoon-texture Decomposition Model and Multi-Scale Filtering Method—0
Interpretable and robust blind image denoising with bias-free convolutional neural networks—0
Interpretable Graph Convolutional Neural Networks for Inference on Noisy Knowledge Graphs—0
Enhanced 3DTV Regularization and Its Applications on Hyper-spectral Image Denoising and Compressed Sensing—0
Enhanced 3D Generation by 2D Editing—0
Contrastive Learning for Low-light Raw Denoising—0
Image Restoration and Reconstruction using Variable Splitting and Class-adapted Image Priors—0
Energy-Inspired Self-Supervised Pretraining for Vision Models—0
Energy Dissipation with Plug-and-Play Priors—0
Image Restoration using Autoencoding Priors—0
Image Restoration Using Conditional Random Fields and Scale Mixtures of Gaussians—0
ARFlow: Autogressive Flow with Hybrid Linear Attention—0
Adversarial Signal Denoising with Encoder-Decoder Networks—0
Denoising-based UNMT is more robust to word-order divergence than MASS-based UNMT—0
Image Restoration using Total Variation Regularized Deep Image Prior—0
Denoising bivariate signals via smoothing and polarization priors—0
A Coordinate Descent Approach to Atomic Norm Denoising—0
Image Restoration with Locally Selected Class-Adapted Models—0
Energy-Based Processes for Exchangeable Data—0
Contrastive CFG: Improving CFG in Diffusion Models by Contrasting Positive and Negative Concepts—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