A combined approach to infrared small target detection with the alternating direction method of multipliers and an improved top-hat transformation

Xi, Tengyan and Yuan, Lihua and Sun, Quanbin (2022) A combined approach to infrared small target detection with the alternating direction method of multipliers and an improved top-hat transformation. Sensors, 22 (19). ISSN 1424-8220

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Abstract

In infrared small target detection, the infrared patch image model (IPI) based methods produce better results than other popular approaches (such as Max-Mean, top-hat, and Human Visual System) but suffer from the long processing times and serious clutters and noises. To tackle such issues, we take a novel approach to divide the traditional target detection process into a background suppression step and a noise and clutter elimination step. The proposed method firstly adapts the alternating direction method of multipliers to preliminarily remove the background. This step does not require sliding patches thus reducing the processing time significantly. The interim results are then processed via an improved new top-hat transformation using a specifically constructed threefold structuring element, to eliminate the residual noises and clutters. The binarised segmentation is done using adaptive thresholds. The experiment results show our method can detect the infrared targets more efficiently and consistently than both the IPI and the new top-hat methods as well as some other widely used methods.

Item Type:	Article
Identification Number:	10.3390/s22197327
Dates:	Date Event 15 September 2022 Accepted 27 September 2022 Published Online
Uncontrolled Keywords:	infrared image; small-target detection; alternating direction method of multipliers; new top-hat; signal to clutter ratio; background suppression factor
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence
Divisions:	Faculty of Computing, Engineering and the Built Environment > College of Computing
Depositing User:	Quanbin Sun
Date Deposited:	26 Sep 2022 10:34
Last Modified:	30 Sep 2022 13:54
URI:	https://www.open-access.bcu.ac.uk/id/eprint/13595

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