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 4726–4750 of 7282 papers

TitleStatusHype
Mixture-Net: Low-Rank Deep Image Prior Inspired by Mixture Models for Spectral Image Recovery—0
Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion—0
Self-Supervised Learning for Speech Enhancement through SynthesisCode0
Self Supervised Low Dose Computed Tomography Image Denoising Using Invertible Network Exploiting Inter Slice Congruence—0
Fast Noise Removal in Hyperspectral Images via Representative Coefficient Total Variation—0
Attention-based Neural Cellular Automata—0
Alternating Phase Langevin Sampling with Implicit Denoiser Priors for Phase Retrieval—0
Unsupervised denoising for sparse multi-spectral computed tomography—0
A new method for determining Wasserstein 1 optimal transport maps from Kantorovich potentials, with deep learning applicationsCode0
Self-supervised Physics-based Denoising for Computed Tomography—0
DensePure: Understanding Diffusion Models towards Adversarial Robustness—0
Denoising neural networks for magnetic resonance spectroscopy—0
DiffusER: Discrete Diffusion via Edit-based Reconstruction—0
LBF:Learnable Bilateral Filter For Point Cloud Denoising—0
Deep network series for large-scale high-dynamic range imaging—0
GeoGCN: Geometric Dual-domain Graph Convolution Network for Point Cloud Denoising—0
Generalized Laplacian Regularized Framelet Graph Neural NetworksCode0
Accelerating Diffusion Models via Pre-segmentation Diffusion Sampling for Medical Image Segmentation—0
ERNIE-ViLG 2.0: Improving Text-to-Image Diffusion Model with Knowledge-Enhanced Mixture-of-Denoising-ExpertsCode0
Topological Slepians: Maximally Localized Representations of Signals over Simplicial ComplexesCode0
Automatic Diagnosis of Myocarditis Disease in Cardiac MRI Modality using Deep Transformers and Explainable Artificial Intelligence—0
Tangent Bundle Filters and Neural Networks: from Manifolds to Cellular Sheaves and Back—0
Optimization for Amortized Inverse Problems—0
Preparing fMRI Data for Statistical Analysis—0
ECG Artifact Removal from Single-Channel Surface EMG Using Fully Convolutional NetworksCode0
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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