为了克服天空背景对红外图像增强处理的干扰、更好地凸显图像中的目标，提出了一种结合脉冲耦合神经网络（PCNN）分割和模糊集理论的红外图像增强方法。利用PCNN将图像分割成天空背景区域和目标区域，之后对目标区域利用变分Retinex处理以获得反射图像，并对该反射图像进行基于岭型分布的自适应模糊增强,将自适应增强后的反射图像与照射图像进行融合，将融合后的目标区域的局部均值赋值给天空背景区域，重构得到增强后的图像。实验结果证明，该方法解决了已有算法中出现的天空区域噪声放大问题，增强后的图像具有更高的对比度和更好的视觉效果。

To overcome the interference of the sky background in infrared image enhancement, and highlight the target in the image, an infrared image enhancement method based on pulse coupled neural network (PCNN) segmentation and fuzzy set theory is proposed. The PCNN is utilized to segment the image into sky background region and target region. The adaptive fuzzy enhancement method based on ridge type distribution is used to enhance the target region reflectance image obtained by variation Retinex, and the enhanced reflectance image and the illumination image are fused together. The local average value of target region is assigned to the sky background region, and the enhanced image is acquired by the reconstruction of the target region and the sky background region. Experimental results demonstrate that the proposed method can overcome the noise amplification problem of existing algorithm in sky region, and the enhanced images have high contrast and good visual effects.