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
收藏
页码：8896 / 8901
页数：6
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