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 5951–5975 of 7282 papers

TitleStatusHype
DeepBeat: A multi-task deep learning approach to assess signal quality and arrhythmia detection in wearable devices—0
Deep Learning on Image Denoising: An overview—0
Opportunities and Challenges of Deep Learning Methods for Electrocardiogram Data: A Systematic ReviewCode0
Scale-wise Convolution for Image RestorationCode1
The Spectral Bias of the Deep Image PriorCode0
Image Restoration using Plug-and-Play CNN MAP DenoisersCode0
PixelRL: Fully Convolutional Network with Reinforcement Learning for Image ProcessingCode0
Towards Robust Toxic Content ClassificationCode0
Variational Coupling Revisited: Simpler Models, Theoretical Connections, and Novel Applications—0
Impulse Denoising From Hyper-Spectral Images: A Blind Compressed Sensing Approach—0
Majorization Minimization Technique for Optimally Solving Deep Dictionary Learning—0
Blind Denoising Autoencoder—0
Deep Learning-based Denoising of Mammographic Images using Physics-driven Data Augmentation—0
DeepMeshFlow: Content Adaptive Mesh Deformation for Robust Image Registration—0
Feature Losses for Adversarial Robustness—0
Basis Prediction Networks for Effective Burst Denoising with Large Kernels—0
Exploiting Model Sparsity in Adaptive MPC: A Compressed Sensing Viewpoint—0
Butterfly-Net2: Simplified Butterfly-Net and Fourier Transform InitializationCode0
In Defense of Uniform Convergence: Generalization via derandomization with an application to interpolating predictors—0
Privacy-Preserving Inference in Machine Learning Services Using Trusted Execution EnvironmentsCode0
Spatial-Frequency Domain Nonlocal Total Variation for Image Denoising—0
KoPA: Automated Kronecker Product Approximation—0
Fully Trainable and Interpretable Non-Local Sparse Models for Image RestorationCode1
Subsurface defect imaging in PZT ceramics using dual point contact excitation and detection—0
Quantum-Inspired Hamiltonian Monte Carlo for Bayesian SamplingCode0
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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