Infrared and visible image fusion via joint convolutional sparse representation
Minghui Wu, Yong Ma, Fan Fan, Xiaoguang Mei, Jun Huang
Abstract
Recently, convolutional sparse representation (CSR) has improved the preservation of details of source images in the fusion results. This is mainly because the CSR has a global representation character that can improve spatial consistency in image representation. However, during image fusion processing, since the CSR expresses infrared and visible images separately, it ignores connections and differences between them. Further, CSR-based image fusion is not able to retain both strong intensity and clear details in the fusion results. In this paper, a novel fusion approach based on joint CSR is proposed. Specifically, we establish a joint form based on the CSR. The joint form is able to guarantee spatial consistency during image representation while obtaining distinct features, such as visible scene details and infrared target intensity. Experimental results illustrate that our fusion framework outperforms traditional fusion frameworks of sparse representation.