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 6051–6075 of 7282 papers

TitleStatusHype
Depth Completion Using a View-constrained Deep Prior—0
Deep Diffusion Models for Seismic Processing—0
Spatial Transport Optimization by Repositioning Attention Map for Training-Free Text-to-Image Synthesis—0
Deep Dose Plugin Towards Real-time Monte Carlo Dose Calculation Through a Deep Learning based Denoising Algorithm—0
Deep Encoder-Decoder Neural Network for Fingerprint Image Denoising and Inpainting—0
Deep End-to-End Time-of-Flight Imaging—0
Generative AI Enables EEG Super-Resolution via Spatio-Temporal Adaptive Diffusion Learning—0
Connections between Deep Equilibrium and Sparse Representation Models with Application to Hyperspectral Image Denoising—0
DeepFN: Towards Generalizable Facial Action Unit Recognition with Deep Face Normalization—0
Deep Gaussian Conditional Random Field Network: A Model-based Deep Network for Discriminative Denoising—0
Deep Generative Models for 3D Medical Image Synthesis—0
Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging—0
Deep Generative Sampling in the Dual Divergence Space: A Data-efficient & Interpretative Approach for Generative AI—0
Deep High-Resolution Network for Low Dose X-ray CT Denoising—0
Deep Identity-aware Transfer of Facial Attributes—0
Deep Image Destruction: Vulnerability of Deep Image-to-Image Models against Adversarial Attacks—0
vec2text with Round-Trip Translations—0
Deep Image Priors for Magnetic Resonance Fingerprinting with pretrained Bloch-consistent denoising autoencoders—0
Deep Interaction between Masking and Mapping Targets for Single-Channel Speech Enhancement—0
Deep Interference Mitigation and Denoising of Real-World FMCW Radar Signals—0
Deep Iterative Reconstruction for Phase Retrieval—0
Deep Joint Denoising and Detection for Enhanced Intracellular Particle Analysis—0
Spatio-Temporal Denoising Graph Autoencoders with Data Augmentation for Photovoltaic Timeseries Data Imputation—0
Deep Kernelized Autoencoders—0
Deep K-SVD Denoising—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