High resoluton image reconstruction: a new imager VIA movable random exposure

Authors:
Guang-Ming Shi;Da-Hua Gao;Dan-Hua Liu;Liang-Jun Wang
Affiliations:
Key Lab. of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, P.R. China;Key Lab. of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, P.R. China and School of Science, Air Force Engineering University, Xi'an, P.R. China;Key Lab. of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, P.R. China;Key Lab. of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, P.R. China
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

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Abstract

Recently compressive sensor developed as an imager for capturing images effectively has been studied extensively. In this paper, we design a new imager to reconstruct high resolution image from a low resolution blurred image obtained by the intended movable random exposure. This imager grabs an image by moving a camera with a randomly fluttering shutter along a certain motion route. By analyzing this kind of movable random exposure process, we find it can be considered as compressive sampling described in the compressive sensing (CS) theory. Then according to the CS theory, the exposure result of this imager can be used to recover a high resolution image. Since this imager consists only a movable camera and a fluttered shutter, it is relatively simple and easy to implement. The simulation results show that the proposed imager can recover high even ultra-high resolution images with good reconstruction performance.