基于自适应Gamma校正的煤岩显微图像增强研究
蒋 敏，奚峥皓*基于自适应Gamma校正的煤岩显微图像增强研究
蒋 敏，奚峥皓*
（上海工程技术大学 电子电气工程学院，上海201620）
摘 要 针对煤岩显微组分在采集过程中存在如光照变化、阴影以及非专业的补光操作所造成识别效果不理想的问题，本文提出一种基于自适应Gamma校正的图像增强方法。该方法主要分为自适应阶段和截断阶段，前者利用像素灰度值的概率分布和Gamma校正来改善模糊图像的对比度，该步骤在图像的暗区能实现良好的增强效果，但在亮区如过度曝光、白色目标和高光区域上无效。为了克服这一缺陷，本文不直接使用累积概率密度函数作为Gamma参数，而是通过设置合理的阈值来截断函数的曲线。因此，可以在不丢失亮区细节的情况下改变图像的对比度。实验结果表明，本文算法能够有效地抑制图像噪声，增强煤岩图像对比度、亮度和提高图像清晰度。
关键词 煤；显微组分；图像增强；Gamma校正
中图分类号：TP391
文献标识码：Adoi:10.3969/j.issn.1000-6281.2020.01.008
The study of coal macerals enhancement based on adaptive Gamma correction
JIANG Min，XI Zheng-hao*
(School of Electronic and Electrical Engineering, Shanghai University of Engineering and Technology, Shanghai 201600, China)
Abstract An image enhancement method based on adaptive Gamma correction is proposed in this paper to solve the problem of non ideal recognition effect in the acquisition process of coal macerals caused by illumination changes, shadows and non-professional fill light operation. . The proposed method is mainly divided into an adaptive phase and a truncation phase. Firstly, we improve the contrast of the blurred image using the probability distribution function of luminance and the Gamma correction. It gains satisfied contrast enhancement in the dark areas of the image，but ineffective on bright regions, such as excessive exposure, white objects and highlight regions. In order to attenuate this deficiency, we truncate curve of the function by setting a reasonable threshold instead of directly applying cumulative probability density function as the Gamma parameter,. Accordingly, contrast of the input images could be improved without losing details in bright regions. The experimental results show that the proposed algorithm can effectively suppress image noise, enhance the contrast, brightness and the image clarity of coal macerals.
Keywords coal；macerals；image enhancement；Gamma correction
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