为了解决局部对比度方法的计算效率低,以及在某些红外场景中易出现虚警的问题,将其与图像区域显著性相结合,提出一种改进的局部对比度算法——区域局部对比度算法,仅在图像的显著性区域中进行局部对比度计算,而非遍历整幅图像。首先进行基于图像信息熵和局部相似性的红外图像区域显著性度量,经二值化得到单帧图像显著性区域；接下来在该区域中进行局部对比度数值计算,得到区域局部对比度图像,最后经过自适应阈值分割,得到弱小目标检测结果。实验结果表明,区域局部对比度算法可以极大提高红外弱小目标的信噪比,检测结果准确,虚警率低,与原始的局部对比度算法相比,检测效率有明显提升,可以更好地保持弱小目标的形状。

A part local contrast measure algorithm is proposed to solve the problem of low efficiency of original local contrast measure, which combines region saliency with original local contrast measure. Instead of finding target in the whole image, the local contrast measure is constrained in saliency region of current image frame in the proposed method. At the first stage, the image entropy and local similarity feature are used to evaluate the saliency of infrared images, which measures the saliency region of a single frame. At the second stage, the local contrast measure is presented in saliency region, which forms the part local contrast map. An adaptive threshold is adopted to segment the target from part local contrast map. Experiments on several real infrared image sequences have validated the ability of the proposed method in improving the signal-to-noise ratio and the detection capability. In particular, the proposed method can reduce false alarm rate, which is an inherent defect of original local contrast method. The high efficiency is also an important strength of the proposed method, which leads to wide application prospect.