A novel infrared small target detection method based on BEMD and local inverse entropy

Compared to other targets, it is more difficult to detect infrared small targets due to several aspects such as the low signal to noise ratio, low contrast, small size, the lack of shape and texture information of the targets, especially under complex background. In this paper, a novel infrared small target detection method based on peer group filter (PGF), bi-dimensional empirical mode decomposition (BEMD) and local inverse entropy (LIE) is proposed to overcome these difficulties. The PGF is implemented to remove the noise and improve the signal-to-noise ratio of the initial image. Our proposed BEMD algorithm is able to estimate the background effectively and get the target image by removing the background from the original image and segmenting the Intrinsic Mode Functions (IMFs) making use of the local inverse entropy. Experimental results demonstrate that the novel method can extract the small targets validly and accurately.