Point spread character based low SNR single pixel infrared target detection. Issue 20 (20th August 2019)

• Record Type: Journal Article
• Authors: Nan, Jinghong; Zhao, Baojun; Tang, Linbo; Zhang, Zengshuo; Tang, Wei
• Is Part Of: Journal of engineering. Volume 2019:Issue 20(2019)
• Page Start: 7042
• Page End: 7046
• Publication Date: 2019-08-20
• Subjects: image enhancement -- image segmentation -- object detection -- infrared imaging -- median filters -- image denoising -- feature extraction -- image filtering; noise disturbance -- detection accuracy -- point‐spread character -- point‐spread indicator -- two‐step enhancement model -- point‐spread local contrast method -- adaptive segmentation method -- background suppression -- low SNR single pixel infrared target detection -- low signal‐to‐noise ratio single pixel IR target detection -- IR searching and tracking system -- potential target protection -- median filter -- noise suppression -- target enhancement -- high boost filter -- target extraction; Engineering -- Periodicals; Engineering; Electronic journals; Periodicals; 620.005
• DOI: 10.1049/joe.2019.0323
• Languages: English
• ISSNs: 2051-3305

Abstract

Abstract : Robust and efficient low signal‐to‐noise ratio (SNR) single pixel infrared (IR) target detection has been a key technique in IR searching and tracking system. However, there exist two critical difficulties, i.e. noise disturbance and low contrast between target and background, influencing detection accuracy. A novel IR target detection method is proposed to overcome those difficulties, by utilising the point‐spread character. Specifically, a point‐spread indicator is advocated to protect the potential target with the median filter to suppress the noise. Afterwards, a two‐step enhancement model is built to increase the contrast value between target region and surrounding background effectively. In the first step, a point‐spread local contrast method is proposed to enhance targets and suppress background. In the second step, a high boost filter is introduced to further enhance the target. In addition, an adaptive segmentation method is exploited to extract targets from the enhanced image. Experimental results on testing sequences demonstrate that this method outperforms the other state‐of‐the‐art methods in single pixel IR target detection with higher detection rate, lower false alarm rate and less time consuming.