基于CLAHE变换的低对比度图像增强改进算法

Low Contrast Image Enhancement Algorithms Based on the Improved CLAHE Transform

在智能交通系统中,天气和光照是图像采集质量的重要客观条件,对后续处理及最终监管有不可忽视的影响。针对雾天和夜间等低对比度图像,提出一种基于有限对比度适应性直方图均衡化的改进算法。该方法首先进行RGB和HIS彩色空间转换,其次仅对亮度分量进行CLAHE变换和非线性拉伸,之后做RGB输出。实验结果表明,该方法在提高对比度同时,良好地保持图像的目标信息,能够提高后续识别和监管的有效性。

In Intelligent Transportation Systems, weather and light are important objective conditions in quality image acquisition, and the impact on the subsequent processing and final supervision cannot be ignored. In this paper, as for fog and low-contrast images at night, an improved algorithm based on the contrast limited adaptive histogram equalization was is proposed. With this method, firstly the RGB and HIS color space conversion was carried out. Secondly CLAHE and non-linear stretching transformation was conducted for only, I component,and then the RGB output was made. Experimental results showed that using this method the contrast was improved and, at the same time, a good image with the target information was maintained. The effectiveness of subsequent identification and supervision could be improved.