基于自适应直方图均衡化的医学图像可逆对比度增强算法  

Medical Image Reversible Contrast Enhancement Based on Adaptive Histogram Equalization

作　　者：谭碰 欧博 TAN Peng;OU Bo(College of Computer Science and Electronic Engineering,Hunan University,Changsha 410082,China)

机构地区：[1]湖南大学信息科学与工程学院,长沙410082

出　　处：《计算机科学》2024年第S01期382-388,共7页Computer Science

基　　金：国家自然科学基金(61872128)。

摘　　要：目前,一些可逆数据隐藏算法通常是对图像进行类似直方图均衡化的信息嵌入操作来实现对比度增强。这类方法虽然简单有效,但是缺乏明确的目标函数来指引参数选择,难以优化对比度增强效果,因而容易产生增强不足或过度增强等问题。为了优化可逆信息嵌入后的对比度增强效果,提出了一种基于自适应直方图均衡化并结合对比度增强的医学图像可逆数据隐藏算法。该方法基于预测误差扩展技术来实现可逆数据嵌入,并通过所定义的自适应直方图均衡化目标函数来优化预测残差直方图的修改,确定最优的数据嵌入点,在确保对比度增强的前提下实现低失真的可逆嵌入。实验结果表明,相比同类算法,所提方法在实现可逆嵌入的同时,能够进一步增强图像对比度,辅助提升医学图像中的目标识别效率。At present,some reversible data hiding algorithms usually conduct the histogram-equalization like data hiding to achieve the contrast enhancement effect for the image.The advantage is that the algorithm is easy to design and conduct.How-ever,it lacks of the optimization objective function,and cannot determine the suitable parameters to optimize the reversible contrast enhancement.As a result,it may suffer the problems of insufficient or excessive enhancement,etc.In order to improve the reversible contrast enhancement effect after data embedding,this paper proposes a reversible data hiding algorithm for medical image combined with contrast enhancement based on adaptive histogram equalization.In the proposed method,the reversible data embedding is implemented by using prediction-error expansion.The objective function of adaptive histogram equalization is designed to optimize the prediction-error histogram modification and determine the optimal embedding positions,by considering the low-distortion embedding and the better contrast enhancement.Experimental results show that compared with other methods,the proposed method can further achieve contrast enhancement effect after reversible data embedding,and therefore improve the target identification of medical image.

关 键 词：医学图像处理 可逆对比度增强 自适应直方图均衡化 预测误差扩展 

分 类 号：TP309.7[自动化与计算机技术—计算机系统结构]