针对红外图像具有对比度低、边缘模糊的缺点, 提出一种基于变分偏微分方程理论的红外图像增强方法。通过重建图像梯度场的分布, 以此增强图像中纹理细节不明显、对比度小的微弱信息。在梯度场的变换中对噪声的幅值切割, 完成了对噪声处理。结果表明本算法使得原图像的灰度平均梯度值提高2～3倍, 对增强红外图像的边缘和微弱有用信息效果明显, 避免了直方图均衡类算法丢失一些灰度相近且分布较少的微弱细节信息的缺点, 为图像识别和跟踪应用提供了高质量的图像信息。

Infrared images typically have the problems of low-contrast and blur edges. This article introduces the variation theory of partial differential equations to enhance the infrared images. By reconstructing image gradient field distribution, detailed texture and low-contrast can be improved. The processing of noise consists the cut of amplitude in the transform of gradient field. Based on experiment results, the original image gray gradient can be increased by 2～3 times and therefore significantly enhances the faint information and alleviates the edge effects. This approach can effectively overcome the problem of losing similar gray scale distribution caused by the histogram class algorithms. Therefore, it gives qualified image information for image recognition and tracking application.