自适应红外目标特征增强算法

利用直方图均衡化和灰度变换增强算法，不能有效增强红外图像目标。鉴于此，在研究红外图像特点的基础上，提出了一种自适应红外目标特征增强算法。该算法先对红外图像进行中值滤波，滤除掉图像中的随机噪声，然后利用直方图分割将红外图像分为目标和背景2部分，通过线性加权叠加抑制背景和增强目标。实验表明，该算法不仅能够根据红外图像中目标的灰度特性自适应地选取直方图分割阈值，而且在去除噪声和增加对比度的同时还抑制了背景，达到了预期的效果。该算法尤其适用于目标和背景像素比例相近时直方图具有局域双峰特征的红外图像中目标的增强。

Adaptive algorithm for infrared target enhancement

Since traditional histogram equalization and gray-scale transformation algorithms could not realize the enhancement of the target in the infrared image effectively, an adaptive algorithm for infrared target enhancement is presented. The algorithm is used to filter out random noise in infrared image by median filtering, and divide the image into target section and background section by histogram, suppress background and enhance target feature by linear weighted overlap. The experimental result shows that this algorithm could automatically choose the threshold of histogram segmentation according to the gray scale characteristic of the target. It can also increase the contrast, decrease the noise and suppress the background. The algorithm performs well for target enhancement in the infrared image whose histogram has two local peaks when target and background pixels are similar.