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

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Showing 6051–6100 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
Spatio-temporal error concealment in video by denoised temporal extrapolation refinement—0
Deep Learning Architecture for Motor Imaged Words—0
Deep Learning Based Channel Covariance Matrix Estimation with User Location and Scene Images—0
Deep Learning-based Denoising of Mammographic Images using Physics-driven Data Augmentation—0
Deep learning based electrical noise removal enables high spectral optoacoustic contrast in deep tissue—0
Deep Learning-Based Extended Target Tracking in ISAC Systems—0
Spatio-temporal Manifold Learning for Human Motions via Long-horizon Modeling—0
Deep Learning-based Modulation Detection for NOMA Systems—0
Deep learning-based statistical noise reduction for multidimensional spectral data—0
Deep Learning-based Symbolic Indoor Positioning using the Serving eNodeB—0
Deep Learning CT Image Restoration using System Blur and Noise Models—0
Deep learning denoiser assisted roughness measurements extraction from thin resists with low Signal-to-Noise Ratio(SNR) SEM images: analysis with SMILE—0
Deep learning denoising for EOG artifacts removal from EEG signals—0
Deep Learning for 3D Point Cloud Enhancement: A Survey—0
Deep learning for full-field ultrasonic characterization—0
Deep Learning for Image Denoising: A Survey—0
Deep Learning for Low-Dose CT Denoising—0
Deep Learning Hyperspectral Image Classification Using Multiple Class-based Denoising Autoencoders, Mixed Pixel Training Augmentation, and Morphological Operations—0
Deep Learning Improves Contrast in Low-Fluence Photoacoustic Imaging—0
Deep-Learning Inversion Method for the Interpretation of Noisy Logging-While-Drilling Resistivity Measurements—0
Deep Learning on Image Denoising: An overview—0
Deep Learning with robustness to missing data: A novel approach to the detection of COVID-19—0
Deeply Aggregated Alternating Minimization for Image Restoration—0
Deeply Cascaded U-Net for Multi-Task Image Processing—0
Deeply Coupled Auto-encoder Networks for Cross-view Classification—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