基于量子和声搜索模糊集的低亮度图像NSCT增强  

Low Brightness Image Enhancement Based on Quantum Harmony Search Fuzzy Sets in NSCT Domain

作　　者：张洁 廖一鹏[2] 戴路 李雪艳 Zhang Jie;Liao Yipeng;Dai Lu;Li Xueyan(College of Artificial Intelligence,Yango Univeraity,Fuzhou,Frjian 350015,China;College of Physica and In formation Engineering.Fruzhou Univeraity.Fuzhou.Fujian 350108,China)

机构地区：[1]阳光学院人工智能学院,福建福州350015 [2]福州大学物理与信息工程学院,福建福州350108

出　　处：《激光与光电子学进展》2021年第24期363-374,共12页Laser & Optoelectronics Progress

基　　金：国家自然科学基金(61471124,61601126);福建省自然科学基金(2019J01224)。

摘　　要：针对低亮度图像存在的对比度低、边缘弱、噪声干扰等问题,提出了一种基于改进量子和声搜索(QHS)算法优化模糊集变换的非下采样Contourlet变换(NSCT)域图像增强方法。首先,将低亮度图像进行NSCT分解,得到低频图像和多尺度高频子带图像。然后,改进QHS算法的量子旋转门更新策略,并将改进的QHS算法用于模糊集变换参数的优化以实现低频图像的自适应增强。接着,根据能量分布对贝叶斯萎缩阈值进行改进以去除高频子带的噪声系数,并通过非线性增益函数实现了边缘和纹理细节的增强。最后,对增强后的各尺度图像进行NSCT重构。对低照度图像、医学计算机断层成像(CT)图像、红外夜视等低亮度图像进行了实验,结果表明,与现有的图像增强方法相比,所提方法不仅改善了图像的整体亮度,还具有更高的信息熵、对比度和清晰度。此外,所提方法在有效抑制噪声的同时保留了更多的纹理细节,且适用于不同环境下的低亮度图像增强。Aiming at the problems of low contrast, weak edges and noise interference of low brightness images, a new image enhancement method based on improved quantum harmony search(QHS) algorithm to optimize fuzzy set transform in nonsubsampled Contourlet transform(NSCT) domain is proposed. First, the low brightness image is subjected to NSCT decomposition to obtain low frequency image and multi-scale high frequency sub-band images. Then, the quantum revolving door updating strategy of QHS algorithm is improved, and the improved QHS algorithm is used to optimize the transformation parameters of the fuzzy sets to realize the adaptive enhancement of low frequency images. Moreover, the Bayesian shrinkage threshold is improved to remove the noise coefficient of the high frequency sub-bands according to the energy distribution, and the edge and texture details are enhanced by the nonlinear gain function. Finally, the enhanced images of various scales are reconstructed by NSCT. Experiments are carried out on low luminance images, medical computed tomography(CT) images and infrared night vision images. The results show that, compared with the existing image enhancement methods, the proposed method not only improves the overall brightness of the image, but also has higher information entropy, contrast and clarity. In addition, the proposed method not only suppresses noise effectively, but also retains more texture details, and is suitable for low brightness image enhancement in different environments.

关 键 词：机器视觉 低亮度图像 图像增强 非下采样CONTOURLET变换 量子和声搜索 模糊集 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]