由于探测器原理性的缺陷，红外热像仪所获图像的对比度低、细节不明显。对比度受限的自适应直方图均衡化方法(Contrast Limited Adaptive Histogram Equalization, CLAHE)能够增强红外图像的对比度，但是其受限参数无法适应不同的场景，且需要手动调整。作为一种智能种群优化算法，布谷鸟搜索算法具备强大的寻优能力。基于该算法，使CLAHE方法在不同场景下获取最优参数，进而实现红外图像对比度的场景自适应增强。实验分析表明，这种改进的算法具有可行性和适应性。

Due to the defect of the detector′s principle, the images taken by the infrared thermal imager have low contrast and unobvious details. Contrast limited adaptive histogram equalization(CLAHE)can enhance the contrast of infrared images. But the parameters need to be adjusted manually since they cannot adapt to different scenes. As an intelligent swarm optimization method, cuckoo search algorithm has a strong ability to search for optimal solutions. In this paper, the cuckoo search algorithm is used to obtain the optimal parameters of CLAHE algorithm in different scenes, and the contrast of infrared images can be adaptive enhanced. The experimental analysis shows that the improved algorithm is feasible and adaptable.