Colorization of fusion image of infrared and visible images based on parallel generative adversarial network approach

Article type: Research Article

Authors: Chen, Lei^{a; *} | Han, Jun^a | Tian, Feng^b

Affiliations: [a] Xi’an Technological University, Xi’an, Shaanxi Province, China | [b] Bournemouth University, Poole, BH12 5BB, UK

Correspondence: [*] Corresponding author. Lei Chen, Xi’an Technological University, Xi’an, Shaanxi Province, 710021, China. E-mail: [email protected].

Abstract: Fusing the infrared (IR) and visible images has many advantages and can be applied to applications such as target detection and recognition. Colors can give more accurate and distinct features, but the low resolution and low contrast of fused images make this a challenge task. In this paper, we proposed a method based on parallel generative adversarial networks (GANs) to address the challenge. We used IR image, visible image and fusion image as ground truth of ‘L’, ‘a’ and ‘b’ of the Lab model. Through the parallel GANs, we can gain the Lab data which can be converted to RGB image. We adopt TNO and RoadScene data sets to verify our method, and compare with five objective evaluation parameters obtained by other three methods based on deep learning (DL). It is demonstrated that the proposed approach is able to achieve better performance against state-of-arts methods.

Keywords: IR and visible images, image fusion, generative adversarial network, lab

DOI: 10.3233/JIFS-210987

Journal: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2255-2264, 2021