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 6926–6950 of 7282 papers

TitleStatusHype
Identity Enhanced Residual Image DenoisingCode0
Observation-Guided Diffusion Probabilistic ModelsCode0
EEGDiR: Electroencephalogram denoising network for temporal information storage and global modeling through Retentive NetworkCode0
Credit Card Fraud Detection Using Autoencoder Neural NetworkCode0
Simultaneous Motion And Noise Estimation with Event CamerasCode0
Addressing Missing Data Issue for Diffusion-based RecommendationCode0
IFH: a Diffusion Framework for Flexible Design of Graph Generative ModelsCode0
IFR-Net: Iterative Feature Refinement Network for Compressed Sensing MRICode0
IIDM: Image-to-Image Diffusion Model for Semantic Image SynthesisCode0
Super-Resolution via Image-Adapted Denoising CNNs: Incorporating External and Internal LearningCode0
Robust Node Classification on Graphs: Jointly from Bayesian Label Transition and Topology-based Label PropagationCode0
ColorFool: Semantic Adversarial ColorizationCode0
Efficient and Flexible Sublabel-Accurate Energy MinimizationCode0
Efficient and Parallel Separable Dictionary LearningCode0
Efficient and principled score estimation with Nyström kernel exponential familiesCode0
AutoJoin: Efficient Adversarial Training against Gradient-Free Perturbations for Robust Maneuvering via Denoising Autoencoder and Joint LearningCode0
MagicPortrait: Temporally Consistent Face Reenactment with 3D Geometric GuidanceCode0
Magicremover: Tuning-free Text-guided Image inpainting with Diffusion ModelsCode0
Brno Mobile OCR DatasetCode0
BRSR-OpGAN: Blind Radar Signal Restoration using Operational Generative Adversarial NetworkCode0
Efficient Blind Deblurring under High Noise LevelsCode0
Magnetogram-to-Magnetogram: Generative Forecasting of Solar EvolutionCode0
Image Blind Denoising Using Dual Convolutional Neural Network with Skip ConnectionCode0
Automated characterization of noise distributions in diffusion MRI dataCode0
Image Compression and Decompression Framework Based on Latent Diffusion Model for Breast MammographyCode0
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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