Learning-based single-shot superresolution in diffractive imaging

被引：8

作者：

Horisaki, Ryoichi ^{[1
,2
]}

Takagi, Ryosuke ^{[1
]}

Tanida, Jun ^{[1
]}

机构：

[1] Osaka Univ, Grad Sch Informat Sci & Technol, Dept Informat & Phys Sci, 1-5 Yamadaoka, Suita, Osaka 5650871, Japan

[2] PRESTO, JST, 4-1-8 Honcho, Kawaguchi, Saitama 3320012, Japan

来源：

APPLIED OPTICS | 2017年 / 56卷 / 32期

基金：

日本学术振兴会;

关键词：

OBJECT RECOGNITION; RECONSTRUCTION;

D O I：

10.1364/AO.56.008896

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

We present a method of retrieving a superresolved object field from a single captured intensity image in diffraction-limited diffractive imaging based on machine learning. In this method, the inverse process of diffractive imaging is regressed by using a number of pairs, each consisting of object and captured images. The proposed method is experimentally demonstrated by using a lensless imaging setup with or without scattering media. (C) 2017 Optical Society of America

引用

页码：8896 / 8901

页数：6

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