1. Compressive hyperspectral imaging with spatial energy distribution information
- Author
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Zhaojun Wu, Rongqiang Zhao, Jing Jin, Yi Shen, Tong Zhao, and Qiang Wang
- Subjects
Engineering ,Energy distribution ,business.industry ,0211 other engineering and technologies ,Hyperspectral imaging ,Sampling (statistics) ,020206 networking & telecommunications ,Pattern recognition ,02 engineering and technology ,Iterative reconstruction ,Field (geography) ,Distortion ,Sampling process ,0202 electrical engineering, electronic engineering, information engineering ,Computer vision ,Artificial intelligence ,business ,Energy (signal processing) ,021101 geological & geomatics engineering - Abstract
Compressive hyperspectral imaging (CHI) has been attracting great interests recently. To reconstruct hyperspectral (HS) images precisely from very few measurements is the main issue considered in CHI field. In this paper we propose a novel and extensible CHI approach including sampling strategy and the corresponding reconstruction method. The spatial energy distribution information (SEDI) can be explored during sampling process where the additional sampling time and measurements created by SEDI are negligible. We develop an SEDI based reconstruction method aiming to eliminate abnormal spikes and correct energy distortion which usually exist in reconstructed HS images. Simulations with real HS data demonstrate the fact that the proposed approach performs better than that without SEDI.
- Published
- 2017