In this paper, we propose a region of interest-based (ROI-adaptive) fusion algorithm of infrared and visible images by using the Laplacian Pyramid method. Firstly, we estimate the saliency map of infrared images, and then divide the infrared image into two parts: the regions of interest (RoI) and the regions of non-interest (nRoI), by normalizing the saliency map. Visible images are also segmented into two parts by using the Gauss High-pass filter: the regions of high frequency (RoH) and the regions of low frequency (RoL). Secondly, we down-sampled both the nRoI of infrared image and the RoL of visible image as the input of next level processing. Finally, we use normalized saliency map of infrared images as the weighted coefficient to get the basic image on the top level and choose max gray value of the RoI of infrared image and the RoH of visible image to get the detail image. In this way, our method can keep target feature of infrared image and texture detail information of visual image at the same time. Experiment results show that such fusion scheme performs better than the other fusion algorithms both on human visual system and quantitative metrics.

Date Published: 4 March 2015
PDF: 5 pages
Proc. SPIE 9443, Sixth International Conference on Graphic and Image Processing (ICGIP 2014), 94431A (4 March 2015); doi: 10.1117/12.2179453

Published in SPIE Proceedings Vol. 9443:
Sixth International Conference on Graphic and Image Processing (ICGIP 2014)
Yulin Wang; Xudong Jiang; David Zhang, Editor(s)