鲁棒的红外小目标视觉显著性检测方法

鲁棒的红外(IR)小目标检测是自动目标检测的关键技术,低信噪比条件下的红外小目标检测一直是业内研究热点.为了能有效地检测红外小目标,对红外小目标若干表观特征及其视觉显著性进行了分析,提出一种基于多尺度图像块统计序对比度(MOCIP)的红外小目标视觉显著性鲁棒检测方法,采用两步级联多尺度图像块统计序对比度算法抑制背景和噪声,提升目标强度,获得红外小目标显著性图,并利用自适应阈值实现目标检测.本文详细给出了红外小目标视觉显著性的检测算法,使用红外小目标图像对检测性能进行了实验验证,并与其他检测方法进行了对比.实验结果表明,所提出的方法能够在低信噪比条件下克服噪声和复杂背景的影响,有效地对红外小目标视觉显著性进行检测.

Robust visual saliency detection method for infrared small target

As a key technique in automatic target detection, robust detection of small infrared target at low signal-to-noise ratio has received a lot of attentions. In order to detect infrared (IR) small targets efficaciously, the apparent characteristics and visual saliency of infrared small target were both analyzed. Thereafter, a robust saliency detection algorithm for infrared small target based on multiscale ordered contrast of image patch (MOCIP) was proposed. The cascaded MOCIP was developed to detect the saliency of infrared small target by suppressing the background and noise and enhancing the infrared small target in two consecutive steps. Consequently, the salient target can be detected with an adaptive threshold in the saliency map obtained by cascaded MOCIP. The robust saliency detection algorithm was presented in details and the efficacy was analyzed. Verification and comparison experiments of detecting the saliency of infrared small target were both conducted. All experiments results show that the proposed MOCIP method is effective and robust in overcoming the impact of noise and complex background and in detecting the saliency of infrared small target at low signal-to-noise (SNR) ratio.