针对传统红外图像动态范围压缩和对比度增强方法对原始图像信息保留不足的缺点，提出了一种基于最优映射曲线的红外图像动态范围压缩和对比度增强方法。利用自适应高斯核函数卷积原始红外图像的直方图，用平滑后直方图的一阶导实现原始直方图灰度分割，用分割结果计算得到最优的非线性映射曲线参数，得到增强后的低动态范围图像。对大量的红外图像进行了实验并对增强效果进行了定性和定量评价。结果表明，相较于目前已有的方法，该方法在最小化图像增强前后均方误差的同时，明显改善了图像的整体视觉效果；该方法处理速度快、自动化程度高，适合红外图像视频的实时处理。

Aiming at the drawback of conventional algorithms to compress dynamic range and enhance infrared images, which cannot preserve sufficient information of the original image, a new method based on the optimal mapping curve is proposed. The histogram of the original infrared image is convolved with an adaptive Gaussian kernel. Then, the first derivative of the smoothed histogram is utilized to accomplish segmentation of the original image. The segmented image is calculated to obtain an optimal nonlinear mapping curve, and then the enhanced image with low dynamic range is obtained. The experiment based on a large amount of infrared images is carried out and the processing results are evaluated qualitatively and quantitatively. The experimental results show that compared with the existing method, the proposed method can minimize the mean square error of the enhanced image compared with its origin. Meanwhile, it can significantly improve the overall visual effect of the image. This algorithm is fast, highly automotive and suitable for real-time processing of infrared videos.