一种结合单尺度Retinex与引导滤波的红外图像增强方法

针对传统红外图像增强算法中图像对比度低、细节信息丢失与过度增强等问题，提出了一种单尺度Retinex与引导滤波相联合的红外图像增强方法。首先根据Retinex算法，利用主特征提取法获取原始图像的照射分量和反射分量，对照射分量采用平台直方图增强其对比度；然后利用局部方差加权引导滤波将反射分量分解为基本层和细节层，对两层分量的图像分别进行对比度和细节增强操作；最后将各个层次的结果按照合适的权重因子进行融合得到增强红外图像。实验结果表明，相比于其他增强算法，本文所提方法能更有效地提高红外图像的整体对比度，突出其细节特征，增强后的3组图像的信息熵和平均梯度平均值分别为9.7373和5.6922，相较于原图像分别提升了2.7499和3.8296。

Research on Infrared Image Enhancement Method Combined with Single-scale Retinex and Guided Image Filter

This study proposes an infrared image enhancement method combined with single scale Retinex and guided image filtering to eliminate the problems of low image contrast, loss of detail information, and excessive enhancement in traditional infrared image enhancement algorithms. First, a structure extraction algorithm is used to obtain the incident and reflected components of the original image according to the Retinex algorithm. The platform histogram is used to enhance the contrast of the incident component. Then, the reflected component is decomposed into the base layer and detail layer by the weighted guided image filter based on variance and by performing contrast and detail enhancement operations on the images of the two components, respectively. Finally, the results of each level are fused according to the appropriate weight factors to obtain an enhanced infrared image. The experiments in this study show that the proposed method can improve the overall contrast of infrared images and highlight their detailed features more effectively than other enhancement algorithms. The information entropy and average gradient of the three groups of images after enhancement are 9.7373 and 5.6922, respectively, which are 2.7499 and 3.8296 higher than the original image.