Single infrared image super‐resolution based on lightweight multi‐path feature fusion network. Issue 7 (24th February 2022)

• Record Type: Journal Article
• Authors: Mo, Fei; Wu, Heng; Qu, Shuo; Luo, Shaojuan; Cheng, Lianglun
• Is Part Of: IET image processing. Volume 16:Issue 7(2022)
• Page Start: 1880
• Page End: 1896
• Publication Date: 2022-02-24
• Subjects: Image processing -- Periodicals; 621.36705
• DOI: 10.1049/ipr2.12454
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract: Single infrared (IR) image super‐resolution methods can help to reduce the cost and difficulty in manufacturing IR sensors for the imaging system. However, the deep learning‐based image SR methods need to build a complex network and thus consume a lot of computational power, which limits the application of SR technology on devices with low computing resources in practice. To solve this problem, the authors present a lightweight multi‐path feature fusion network (MFFN) for the single infrared (IR) image SR. A multi‐path feature fusion block (MFFB) is developed to extract and fuse multiple and discriminative features in a recursive feedback way. Specifically, the multiple features are refined via the linear feature extraction branch, shared‐source residual feature extraction branch, and channel attention branch in MFFB. Finally, the authors reconstruct the high‐resolution IR images from the low‐resolution counterpart based on the refined multiple features. The experimental results demonstrate that MFFN achieves high‐quality single infrared image SR and shows superiority over previous methods for several scale factors (e.g. ×2, ×3, and ×4). MFFN has potential applications in the mobile infrared imaging system.