Title: A novel visible-infrared image fusion framework for smart city

Authors: Zhiqin Zhu; Guanqiu Qi; Yi Chai; Hongpeng Yin; Jian Sun

Addresses: School of Automation, Chongqing University, Chongqing, 400044, China; State Key Laboratory of Power Transmission Equipment and System Security and New Technology, College of Automation, Chongqing University, Chongqing, 400044, China ' School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, Arizona, 85287, USA ' School of Automation, Chongqing University, Chongqing, 400044, China;State Key Laboratory of Power Transmission Equipment and System Security and New Technology, College of Automation, Chongqing University, Chongqing, 400044, China ' School of Automation, Chongqing University, Chongqing, 400044, China;Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing, 400030, China ' School of Automation, Chongqing University, Chongqing, 400044, China;Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing, 400030, China

Abstract: Image fusion technology is widely used in different areas and can integrate complementary and relevant information of source images captured by multiple sensors into a unitary synthetic image. Image fusion technology as an efficient way to integrate information from multiple images plays a more and more important role in smart city. The quality of fused image affects the accuracy, efficiency, and robustness of the related applications. Existing sparse representation-based image fusion methods consist of overly complete and redundant dictionaries learning and sparse coding. However, overly complete and redundant dictionary does not consider the discriminative ability of dictionaries that may seriously affect the image fusion. A good dictionary is the key to a successful image fusion technique. To construct a discriminative dictionary, a novel framework that integrates an image-patches clustering and online dictionary learning methods is proposed for visible-infrared image fusion. The comparison experiments with existing solutions are used to validate and demonstrate the effectiveness of the proposed solution for image fusion.

Keywords: image fusion; sparse representation; dictionary learning; sub-space clustering; smart city.

DOI: 10.1504/IJSPM.2018.091691

International Journal of Simulation and Process Modelling, 2018 Vol.13 No.2, pp.144 - 155

Received: 08 Oct 2015
Accepted: 16 Feb 2016
Published online: 14 May 2018 *

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