极值温差红外图像的自适应双局部增强算法

Adaptive bilocal enhancement algorithm for infrared image with extreme temperature difference

针对目前红外焦平面成像系统在观察目标、特别是极值温差目标时,各温度段灰度描述不均匀和细节不够的问题,提出了一种自适应红外图像双局部增强算法。详细介绍了通过空间分布和灰度统计特性两个方向实现对极值温差图像自适应增强的方法,该方法首先从红外图像的空间分布特性出发,将图像切割成多个局部图像,然后再从直方图灰度分布出发,将局部图像的直方图进行聚类分段,并对分段直方图均衡增强,最后对生成的每个局部图像增强结果进行线性插值拼接完成增强算法。通过在红外焦平面系统中实验证明了极值温差自适应的红外图像双局部增强算法的可行性,并获得了很好的效果,成像质量有明显提高。

The infrared focal plane system can＇t provide well distributed gray of every temperature section and sufficient im- age details for targets with extreme temperature difference. To address this problem, an adaptive bilocal enhancement algorithm was proposed. This paper gives a detailed introduction about the adaptive bilocal enhancement algorithm from two aspects., spatial distribution and gray statistics characteristics. This algorithm adaptively divides the original image into several sub-images based on the spatial distribution of infrared image, then gets the local histogram statistics based on the local gray statistics characteris- tics, and do adaptive segment equalization on the histogram of every local space. At last, the enhanced images were mosaicked by linear interpolation algorithm. The practicability of this algorithm has been verified by a series of experiments on the infrared focal plane system. The effect was satisfactory and the imaging quality was improved significantly.