Open AccessDeeply coded aperture for lensless imaging
Author(s) -
Ryoichi Horisaki,
Yuka Okamoto,
Jun Tanida
Publication year - 2020
Publication title -
optics letters/optics index
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.524
H-Index - 272
eISSN - 1071-2763
pISSN - 0146-9592
DOI - 10.1364/ol.390810
Subject(s) - coded aperture , optics , computer science , convolutional neural network , aperture (computer memory) , artificial intelligence , iterative reconstruction , physics , detector , acoustics
In this Letter, we present a method for jointly designing a coded aperture and a convolutional neural network for reconstructing an object from a single-shot lensless measurement. The coded aperture and the reconstruction network are connected with a deep learning framework in which the coded aperture is placed as a first convolutional layer. Our co-optimization method was experimentally demonstrated with a fully convolutional network, and its performance was compared to a coded aperture with a modified uniformly redundant array.