Small target detection using edge-preserving background estimation based on maximum patch similarity. (1st December 2017)

• Record Type: Journal Article
• Authors: Wang, Yuehuan; Xu, Xueping; Yue, Nuoning; Chen, Jie
• Is Part Of: International journal of advanced robotic systems. Volume 14:Number 6(2017:Nov./Dec.)
• Publication Date: 2017-12-01
• Subjects: Improved local adaptive contrast measure -- maximum patch similarity -- edge-preserving background estimation -- adaptive threshold segmentation -- small target detection; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881417744822
• Languages: English
• ISSNs: 1729-8806

Abstract

Infrared small target detection is widely applied in lots of practical applications, but due to the complicated edges in practical scenarios, most existing detection algorithms usually lead to many false alarms and cannot detect the target accurately. Addressing this problem, a novel edge-preserving background estimation method based on maximum patch similarity was proposed in this article. At first, we will propose an improved local adaptive contrast measure to suppress the pixel-size electronic noises. Then, maximum patch similarity with minimum improved local adaptive contrast measure can be utilized to preserve the edge in the estimated background. Finally, we can obtain target image by filtering the background image from original image and use adaptive threshold segmentation to detect the small target in our target image. It is shown from experiments that our proposed method has better detection results in diverse infrared images, improving signal-to-clutter ratio gain and background suppression factor of the images significantly and efficiently.