Scale Adaptive Infrared Small Target Detection with Patch Contrast Measure

Robust infrared small target detection in infrared warning and defending system is a challenging task due to the low signal-to-clutter ratios and complex background. Motivated by human vision system, we proposed a scale adaptive patch-based contrast measure(SPCM) method for infrared small target detection. At the first stage, a patch-based contrast model is established for measuring small target scale response, whose highest response corresponding to the best estimated size of the target, at the same time, filtering out some non-target areas and leave potential candidates. At the Second stage, we calculate local patch contrast at candidate regions with the estimated target scale. Utilizing the just right scale, the patch-based contrast measure could effectively suppress background clutter and extract infrared small target in single image. Finally, an improved adaptive threshold method by using statistical information of candidate target is used to segment infrared small target. In order to verify the effectiveness of the proposed approach, we compared our method with several fixed-scale and multi-scale infrared small detection algorithm. Experimental results indicate that our method is not only able to effectively estimate the actual scale of the target, but also detect weak small target accurately in heterogeneous background with low and comparable false alarm ratio, while achieving three times faster runtime performance than multi-scale algorithm.