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 3501–3550 of 7282 papers

TitleStatusHype
A Variational Step for Reduction of Mixed Gaussian-Impulse Noise from Images—0
Aligning Foundation Model Priors and Diffusion-Based Hand Interactions for Occlusion-Resistant Two-Hand Reconstruction—0
Graph with Sequence: Broad-Range Semantic Modeling for Fake News Detection—0
Multiscale Structure Guided Diffusion for Image Deblurring—0
Graph Unrolling Networks: Interpretable Neural Networks for Graph Signal Denoising—0
Image Deconvolution with Deep Image and Kernel Priors—0
Image Denoising and Inpainting with Deep Neural Networks—0
Image denoising and restoration with CNN-LSTM Encoder Decoder with Direct Attention—0
Image Denoising and Super-Resolution using Residual Learning of Deep Convolutional Network—0
Deep Learning for Low-Dose CT Denoising—0
Image denoising as a conditional expectation—0
Image denoising based on improved data-driven sparse representation—0
Image Denoising by Gaussian Patch Mixture Model and Low Rank Patches—0
Image denoising by Super Neurons: Why go deep?—0
Graph Spatio-Spectral Total Variation Model for Hyperspectral Image Denoising—0
Image denoising in acoustic field microscopy—0
Image Denoising in FPGA using Generic Risk Estimation—0
Image Denoising Inspired by Quantum Many-Body physics—0
Image Denoising: The Deep Learning Revolution and Beyond -- A Survey Paper --—0
Image denoising through bivariate shrinkage function in framelet domain—0
Deep Learning for Image Denoising: A Survey—0
Graph Sparsification for Enhanced Conformal Prediction in Graph Neural Networks—0
Image Denoising Using Convolutional Autoencoder—0
Graph Signal Restoration Using Nested Deep Algorithm Unrolling—0
Deep learning for full-field ultrasonic characterization—0
Graphs as Tools to Improve Deep Learning Methods—0
Deep Learning for 3D Point Cloud Enhancement: A Survey—0
Image Denoising using New Adaptive Based Median Filters—0
A Variational Approach to Shape-from-shading Under Natural Illumination—0
Image Denoising Using Sparsifying Transform Learning and Weighted Singular Values Minimization—0
Graph Sanitation with Application to Node Classification—0
Image Denoising via CNNs: An Adversarial Approach—0
Graph Representation Learning with Diffusion Generative Models—0
Image Denoising Via Collaborative Support-Agnostic Recovery—0
Image denoising via group sparsity residual constraint—0
Image denoising via K-SVD with primal-dual active set algorithm—0
Deep learning denoising for EOG artifacts removal from EEG signals—0
Image Denoising via Style Disentanglement—0
Graph Neural Networks and Differential Equations: A hybrid approach for data assimilation of fluid flows—0
Deep learning denoiser assisted roughness measurements extraction from thin resists with low Signal-to-Noise Ratio(SNR) SEM images: analysis with SMILE—0
Auxiliary Features-Guided Super Resolution for Monte Carlo Rendering—0
Image Denoising with Kernels based on Natural Image Relations—0
Image denosing in underwater acoustic noise using discrete wavelet transform with different noise level estimation—0
Image Deraining and Denoising Convolutional Neural Network ForAutonomous Driving—0
Graph Laplacian Regularization for Image Denoising: Analysis in the Continuous Domain—0
Image Edge Restoring Filter—0
Graphics2RAW: Mapping Computer Graphics Images to Sensor RAW Images—0
Image enhancement in acoustic-resolution photoacoustic microscopy enabled by a novel directional algorithm—0
Graph Generation via Spectral Diffusion—0
Deep Learning CT Image Restoration using System Blur and Noise Models—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