Retinex理论下基于融合思想的低照度彩色图像增强算法

针对Retinex算法处理后的图像边缘保持性差,易产生光晕和过增强的缺点,以及双边滤波易造成图像细节丢失的现象,提出了一种Retinex理论下基于融合思想的图像增强算法。该算法首先在YCb Cr颜色空间提取亮度Y分量,对亮度分量进行大、中、小不同尺度的MSR增强,获得细节信息保留较好的亮度图像,同时在RGB颜色空间对图像进行基于双边滤波的单尺度Retinex增强,获得边缘信息较好的增强图像;然后对增强后的两幅图像加权融合;最后对融合的图像进行颜色恢复处理得到最终的增强图像。通过本文算法与经典SSR、MSR和MSRCR算法处理后的图像进行比较,实验结果表明,本文算法处理后的图像在细节、颜色和边缘保持方面都优于其他算法,并且避免了光晕和过增强现象的发生。

Low Illumination Color Image Enhancement Algorithm Based on Fusion Idea under Retinex Theory

Aiming at the defects of the Retinex algorithm, such as poor edge preservation, easy to produce halo and over-enhancement, and the phenomenon that bilateral filtering is easy to cause the loss of detail of the image, an image enhancement algorithm based on fusion idea under Retinex theory is proposed. The algorithm firstly extracts the luminance Y component in the YCbCr color space, and performs MSR enhancement on the large, medium and small scales of the luminance component to obtain a brightness image with better information preservation. At the same time, the single-scale Retinex enhancement based on bilateral filtering is performed in RGB color space to obtain the enhanced image with better edge information. Then, the enhanced two images are weighted and fused. Finally, the fused image is processed by color restoration to obtain the final enhanced image. Compared with the images processed by this algorithm, the classical SSR, MSR and MSRCR algorithms, the experimental results show that the images processed by this algorithm are superior to other algorithms in details, color and edge preservation, and avoid the occurrence of halo and over-enhancement.