改进的自适应伽马变换图像增强算法仿真  

Image Enhancement Algorithm Simulation Based on Improved Adaptive Gamma Transformation

作　　者：杨先凤[1] 李小兰 贵红军 YANG Xian-feng;LI Xiao-lan;GUI Hong-jun(College of Computer Science,Southwest Petroleum University,Chengdu Sichuan 610500,China)

机构地区：[1]西南石油大学计算机科学学院,四川成都610500

出　　处：《计算机仿真》2020年第5期241-245,共5页Computer Simulation

基　　金：国家自然科学青年基金项目(61503312)。

摘　　要：针对传统方法在处理低质量图像时出现的对比度低、亮度过亮等问题,提出一种改进的自适应伽马变换图像增强算法。用图像对比度为标准,将图像分为低对比度图像及中等对比度图像。构造一种改进的伽马函数,利用图像局部信息自适应确定伽马变换的参数,对相应图像进行亮度和对比度提高。实验结果表明,算法可以弥补其它参考算法处理结果亮度值过高、对比度低的不足,有效改善了图像的亮度和对比度,且适用于低对比度、低照度图像、医学MRI图像等多种低质图像。Aiming at the problems of low contrast and bright brightness when dealing with low quality image in traditional methods, an improved adaptive gamma transform image enhancement algorithm is proposed. The image was divided into a low-contrast image and a medium-contrast image using the image contrast as a standard. Then, an improved gamma function was constructed, and the parameters of the gamma transform were adaptively determined based on the image local information, and the brightness and contrast of the corresponding image was improved. The experimental results show that the algorithm can make up for the lack of high brightness value and low contrast of other reference algorithm processing results, which effectively improves the brightness and contrast of the image, and is suitable for low-contrast images such as low-contrast, low-light and medical MRI images.

关 键 词：图像增强 伽马变换 低对比度 低照度 自适应 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]