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 5576–5600 of 7282 papers

TitleStatusHype
Accelerated Gradient Methods for Sparse Statistical Learning with Nonconvex PenaltiesCode0
Joint ptycho-tomography with deep generative priorsCode1
Deep Autoencoders: From Understanding to Generalization Guarantees—0
Inferring, Predicting, and Denoising Causal Wave Dynamics—0
Improving the spatial resolution of a BOTDA sensor using deconvolution algorithm—0
Simultaneous Denoising and Motion Estimation for Low-dose Gated PET using a Siamese Adversarial Network with Gate-to-Gate Consistency Learning—0
Joint Demosaicking and Denoising Benefits from a Two-stage Training Strategy—0
Accurate and Lightweight Image Super-Resolution with Model-Guided Deep Unfolding Network—0
Robust Deep Learning Ensemble against Deception—0
Clinically Translatable Direct Patlak Reconstruction from Dynamic PET with Motion Correction Using Convolutional Neural Network—0
Deep learning denoising for EOG artifacts removal from EEG signals—0
Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from DataCode1
Unit Test Case Generation with Transformers and Focal ContextCode1
Adversarial score matching and improved sampling for image generationCode1
Error analysis for denoising smooth modulo signals on a graph—0
Denoising Large-Scale Image Captioning from Alt-text Data using Content Selection Models—0
Self-supervised Depth Denoising Using Lower- and Higher-quality RGB-D sensors—0
Denoising modulo samples: k-NN regression and tightness of SDP relaxationCode0
Enhancing and Learning Denoiser without Clean Reference—0
Blind Image Restoration with Flow Based Priors—0
Aircraft engines Remaining Useful Life prediction with an adaptive denoising online sequential Extreme Learning MachineCode0
A Residual Solver and Its Unfolding Neural Network for Total Variation Regularized Models—0
E-BERT: A Phrase and Product Knowledge Enhanced Language Model for E-commerce—0
Adversarial Watermarking Transformer: Towards Tracing Text Provenance with Data HidingCode1
Are Deep Neural Architectures Losing Information? Invertibility Is IndispensableCode1
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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