基于图像融合技术的Retinex图像增强算法

针对单尺度Retinex图像增强算法存在的光晕现象和图像泛灰问题，提出一种基于图像融合技术的Retinex图像增强算法。针对光晕现象，使用高斯加权双边滤波代替单尺度Retinex算法中的高斯核函数估计光照图像，能够有效去除光晕现象。针对图像泛灰问题，引入图像融合的思想。首先，采用非线性变换拉伸反射图像，并通过Otsu阈值分割算法确定图像的亮、暗区域；然后，以信息熵为标准，通过调整非线性变换的参数来获得亮区域最优图像和暗区域最优图像，并将原始图像、亮区域最优图像和暗区域最优图像采用分块融合的方法进行融合；最后，为克服图像分块融合算法的块效应，在融合过程中加入一致性校验。实验结果表明，新算法能够充分获得图像的细节信息，同时有效去除光晕现象、改善图像泛灰的不足。相比于单尺度Retinex算法、基于双边滤波的Retinex算法、直方图均衡算法以及反锐化掩膜算法，新算法的图像增强能力具有显著的提升。

A Retinex image enhancement algorithm based on image fusion technology

We propose a Retinex image enhancement algorithm based on image fusion technology to overcome the disadvantages of halo phenomenon and grey phenomenon of the single scale Retinex algorithm. For the halo phenomenon, the illumination image of an original image is estimated by Gaussian weighted bilateral filtering instead of Gaussian kernel function, which can remove the halo phenomenon effectively. For the problem of grey, the image fusion technology is introduced into the process of image enhancement. Here are the several steps of the proposed algorithm. In the first place, the reflection image is stretched by nonlinear transformation and the bright and dark areas of the stretched image are determined by the Otsu threshold segmentation algorithm. Then the optimal image for bright areas and the optimal image for dark areas are obtained by adjusting the parameters of nonlinear transformation according to information entropy, and the original image, the optimal image for bright areas and the optimal image for dark areas are fused by the optimal fusion algorithm. Finally, a consistency verification method is introduced to remove the blocking effect caused by the optimal fusion algorithm. Experimental results show that the new algorithm can get the details of an image, remove the halo phenomenon and overcome the grey problem effectively. It also has a better ability to enhance images compared with the single scale Retinex algorithm, Retinex algorithm based on bilateral filtering, histogram equalization and unshaped mask algorithm.