Colorization of fusion image of infrared and visible images based on parallel generative adversarial network approach
Authors: Chen, L., Han, J. and Tian, F.
Journal: Journal of Intelligent and Fuzzy Systems
Volume: 41
Issue: 1
Pages: 2255-2264
eISSN: 1875-8967
ISSN: 1064-1246
DOI: 10.3233/JIFS-210987
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.
https://eprints.bournemouth.ac.uk/35970/
Source: Scopus
Colorization of fusion image of infrared and visible images based on parallel generative adversarial network approach
Authors: Chen, L., Han, J. and Tian, F.
Journal: JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
Volume: 41
Issue: 1
Pages: 2255-2264
eISSN: 1875-8967
ISSN: 1064-1246
DOI: 10.3233/JIFS-210987
https://eprints.bournemouth.ac.uk/35970/
Source: Web of Science (Lite)
Colorization of fusion image of infrared and visible images based on parallel generative adversarial network approach
Authors: Chen, L., Han, J. and Tian, F.
Journal: Journal of Intelligent and Fuzzy Systems
Volume: 41
Issue: 1
Pages: 2255-2264
ISSN: 1064-1246
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.
https://eprints.bournemouth.ac.uk/35970/
Source: BURO EPrints