A Method for Weak Target Detection Based on Human Visual Contrast Mechanism

Abstract

In order to detect space weak targets in low signal-to-clutter ratio (SCR) environment, this letter presents an effective detection model. At the first stage, two-dimensional least-mean-square preprocessing part is applied to the original image, after which, the suspicious area can be obtained. At the second stage, a novel method based on the contrast mechanism of human visual system called neighborhood saliency map is applied, which improves the local contrast map algorithm and greatly increases the accuracy of neighborhood saliency estimation, meanwhile, effectively enhances targets and suppresses background. Then, a simple threshold segmentation is used to get real targets. Compared with other state-of-the-art algorithms, the proposed algorithm obtains the superior performance in terms of SCR gain, background suppression factor, and detection results (detection rate and false alarm rate). The model proposed in this letter can effectively detect spatial weak point targets in the case of average SCR ≈ 1 or even SCR <; 1 (with minimum SCR is about 0.55).