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 3126–3150 of 7282 papers

TitleStatusHype
Aerial Active STAR-RIS-assisted Satellite-Terrestrial Covert Communications—0
Deep Camera: A Fully Convolutional Neural Network for Image Signal Processing—0
Image Deraining via Self-supervised Reinforcement Learning—0
Autoencoders as Weight Initialization of Deep Classification Networks for Cancer versus Cancer Studies—0
Generating Synthetic Data for Task-Oriented Semantic Parsing with Hierarchical Representations—0
HiddenSinger: High-Quality Singing Voice Synthesis via Neural Audio Codec and Latent Diffusion Models—0
Generating Synthetic Net Load Data with Physics-informed Diffusion Model—0
Error Correction in ASR using Sequence-to-Sequence Models—0
Generative AI-Based Probabilistic Constellation Shaping With Diffusion Models—0
Error Bounds for Flow Matching Methods—0
A Self-Supervised Algorithm for Denoising Photoplethysmography Signals for Heart Rate Estimation from Wearables—0
Error analysis for denoising smooth modulo signals on a graph—0
On Convex Duality in Linear Inverse Problems—0
AE-RED: A Hyperspectral Unmixing Framework Powered by Deep Autoencoder and Regularization by Denoising—0
Generative AI Meets Semantic Communication: Evolution and Revolution of Communication Tasks—0
A Cross Validation Framework for Signal Denoising with Applications to Trend Filtering, Dyadic CART and Beyond—0
Generative artificial intelligence in ophthalmology: multimodal retinal images for the diagnosis of Alzheimer's disease with convolutional neural networks—0
ERNet Family: Hardware-Oriented CNN Models for Computational Imaging Using Block-Based Inference—0
Generative Class-conditional Autoencoders—0
ERD: Exponential Retinex decomposition based on weak space and hybrid nonconvex regularization and its denoising application—0
Generative diffusion model surrogates for mechanistic agent-based biological models—0
Convex Dual Theory Analysis of Two-Layer Convolutional Neural Networks with Soft-Thresholding—0
Generative Diffusion Prior for Unified Image Restoration and Enhancement—0
Generative Edge Detection with Stable Diffusion—0
ERA-Solver: Error-Robust Adams Solver for Fast Sampling of Diffusion Probabilistic Models—0
Show:102550
← PrevPage 126 of 292Next →
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