Open AccessContent-adaptive ghost imaging of dynamic scenes
Author(s) -
Ziwei Li,
Jinli Suo,
Xuemei Hu,
Qionghai Dai
Publication year - 2016
Publication title -
optics express
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.24.007328
Subject(s) - ghost imaging , computer science , redundancy (engineering) , computer vision , artificial intelligence , frame (networking) , image quality , dynamic imaging , iterative reconstruction , pixel , temporal resolution , image resolution , frame rate , image processing , optics , image (mathematics) , physics , digital image processing , telecommunications , operating system
Limited by long acquisition time of 2D ghost imaging, current ghost imaging systems are so far inapplicable for dynamic scenes. However, it's been demonstrated that nature images are spatiotemporally redundant and the redundancy is scene dependent. Inspired by that, we propose a content-adaptive computational ghost imaging approach to achieve high reconstruction quality under a small number of measurements, and thus achieve ghost imaging of dynamic scenes. To utilize content-adaptive inter-frame redundancy, we put the reconstruction under an iterative reweighted optimization, with non-uniform weight computed from temporal-correlated frame sequences. The proposed approach can achieve dynamic imaging at 16fps with 64×64-pixel resolution.