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 2401–2425 of 7282 papers

TitleStatusHype
Algorithm-Induced Prior for Image Restoration—0
Estimating User Location in Social Media with Stacked Denoising Auto-encoders—0
Automatic Foreground Extraction from Imperfect Backgrounds using Multi-Agent Consensus Equilibrium—0
Adaptive Denoising of Signals with Local Shift-Invariant Structure—0
Deep Generative Sampling in the Dual Divergence Space: A Data-efficient & Interpretative Approach for Generative AI—0
Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging—0
Estimating LASSO Risk and Noise Level—0
Evaluating BM3D and NBNet: A Comprehensive Study of Image Denoising Across Multiple Datasets—0
Evaluation of Denoising Techniques for EOG signals based on SNR Estimation—0
Event-Guided Denoising for Multilingual Relation Learning—0
Deep Generative Models for 3D Medical Image Synthesis—0
Automatic Diagnosis of Myocarditis Disease in Cardiac MRI Modality using Deep Transformers and Explainable Artificial Intelligence—0
Automatic Detection of ECG Abnormalities by using an Ensemble of Deep Residual Networks with Attention—0
Deep Gaussian Conditional Random Field Network: A Model-based Deep Network for Discriminative Denoising—0
DeepFN: Towards Generalizable Facial Action Unit Recognition with Deep Face Normalization—0
ESPnet-se: end-to-end speech enhancement and separation toolkit designed for asr integration—0
Automatic artifact removal of resting-state fMRI with Deep Neural Networks—0
A Lesson in Splats: Teacher-Guided Diffusion for 3D Gaussian Splats Generation with 2D Supervision—0
Connections between Deep Equilibrium and Sparse Representation Models with Application to Hyperspectral Image Denoising—0
Error mitigation of entangled states using brainbox quantum autoencoders—0
Establishing strong imputation performance of a denoising autoencoder in a wide range of missing data problems—0
Deep End-to-End Time-of-Flight Imaging—0
Automated OCT Segmentation for Images with DME—0
Deep Encoder-Decoder Neural Network for Fingerprint Image Denoising and Inpainting—0
A Lennard-Jones Layer for Distribution Normalization—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