Image Enhancement-Based Detection with Small Infrared Targets

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2022-07, Vol.14 (13), p.3232
• Main Authors: Liu, Shuai, Chen, Pengfei, Woźniak, Marcin
• Format: Article
• Language: English
• Subjects: Algorithms; autonomous systems; Background noise; Clutter; Deep learning; Flooding; Image contrast; Image enhancement; infrared small target; Interpolation; Mutation; Neural networks; perception capabilities; Pixels; Remote sensing; small target detection; Target detection; Teaching methods; upsampling
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14133232

Today, target detection has an indispensable application in various fields. Infrared small-target detection, as a branch of target detection, can improve the perception capability of autonomous systems, and it has good application prospects in infrared alarm, automatic driving and other fields. There are many well-established algorithms that perform well in infrared small-target detection. Nevertheless, the current algorithms cannot achieve the expected detection effect in complex environments, such as background clutter, noise inundation or very small targets. We have designed an image enhancement-based detection algorithm to solve both problems through detail enhancement and target expansion. This method first enhances the mutation information, detail and edge information of the image and then improves the contrast between the target edge and the adjacent pixels to make the target more prominent. The enhancement improves the robustness of detection with background clutter or noise-flooded scenes. Moreover, bicubic interpolation is used on the input image, and the target pixels are expanded with upsampling, which enhances the detection effectiveness for tiny targets. From the results of qualitative and quantitative experiments, the algorithm proposed in this paper outperforms the existing work on various evaluation indicators.