A novel approach of infrared small weak target detection based on an improved nonsubsampled contourlet transform

Infrared small weak target is difficult to detect since it often take up one or two pixel(s) in image. Nonsubsampled contourlet transform based detection algorithms work well in this case, but suffer from expensive time cost. In this paper, a novel infrared weak small target detection algorithm using optimized nonsubsampled contourlet transform is presented. We adopt an optimized nonsubsampled contourlet transform in which marking feature is faster than the original transform. First, raw images are decomposed into low frequency and high frequency coefficients by the optimized nonsubsampled contourlet, then the adaptive mean filter and the Gabor wavelet are used to suppress the background information respectively, in order to eliminate a small quantity of high light points from background, region entropy is used to distinguish background points, considering as false target alarms if not detected and true targets. Experimental results demonstrate that the presented algorithm can detect the infrared weak small targets effectively.