基于改进的教与学优化算法的图像增强方法  

An image enhancement method based on improved teaching-learning-based optimization algorithm

作　　者：毕晓君[1] 潘铁文[1] 

机构地区：[1]哈尔滨工程大学信息与通信工程学院,黑龙江哈尔滨150001

出　　处：《哈尔滨工程大学学报》2016年第12期1716-1721,共6页Journal of Harbin Engineering University

基　　金：国家自然科学基金项目(61175126)

摘　　要：为改善图像质量,并使处理后的图像便于后续处理工作,提出一种基于改进的教与学优化算法的图像增强方法。结合图像局部信息和全局信息将原始图像进行转换,并建立图像增强优化模型和包括了边缘强度、边缘像素和二维熵等重要信息的评价函数。对教与学优化算法进行两点改进:一方面自适应调整教学因子,充分协调种群的多样性和收敛性,提高全局搜索能力和收敛精度;另一方面通过最优个体引导机制,加快收敛速度。最后,将提出的教与学优化算法用于图像增强,来提高图像对比度。实验结果表明,相比于其他方法,本文算法获得更佳的视觉效果和图像质量。To improve image quality and render the enhanced image more suitable for subsequent image processing,an image enhancement method based on improved teaching-learning-based optimization（TLBO） algorithm is pres-ented. First, combining local information with global information, the original image is converted into the enhanced image. Subsequently, an image enhancement optimization model and an evaluation function including edge intensi-ty, edge pixels, and entropy were established. Second, the TLBO algorithm was modified in two aspects： to raise the global search capability and convergence precision the teaching factor was adaptively adjusted for coordinating the diversity and convergence of the population, and an optimal individual guidance mechanism was produced to speed up the convergence. The suggested TLBO was first applied to optimize the image enhancement optimization model. Experiment results show that compared with other methods, the proposed method has better visual effects and image quality.

关 键 词：教与学优化 图像增强 对比度 视觉效果 评价函数 最优引导 多样性 收敛性 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]