Optimal Transport for Super Resolution Applied to Astronomy Imaging

被引：0

作者：

Rawson, Michael ^{[1
,2
]}

Hultgren, Jakob ^{[3
]}

机构：

[1] Univ Maryland CP, Pacific Northwest Natl Lab, College Pk, MD 20742 USA

[2] Univ Maryland CP, Dept Math, Cp, MD 20742 USA

[3] Univ Maryland, Dept Math, College Pk, MD USA

来源：

2022 30TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2022) | 2022年

关键词：

optimal transport; Wasserstein distance; super resolution; compressed sensing; sparse imaging; sparse regularization; sparsity; maximum entropy; convolutional neural network; DECONVOLUTION;

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

Super resolution is an essential tool in optics, especially on interstellar scales, due to physical laws restricting possible imaging resolution. We propose using optimal transport and entropy for super resolution applications. We prove that the reconstruction is accurate when sparsity is known and noise or distortion is small enough. We prove that the optimizer is stable and robust to noise and perturbations. We compare this method to a state of the art convolutional neural network and get similar results for much less computational cost and greater methodological flexibility.

引用

页码：1971 / 1975

页数：5

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