一种基于杜鹃搜索算法的图像自适应增强方法
|
摘要
图像增强是图像处理中关键步骤,基于归一化的非完全Beta函数变换的图像增强具有理想的增强效果。然而合理选取归一化的非完全Beta函数的参数是算法的关键和难点,常需要人工干预或是计算非常耗时。杜鹃搜索算法是一种新型的仿生智能算法,具有自适应、自组织等智能特性,具有强大的寻找优化解的能力。这里将杜鹃搜索算法用于归一化的非完全Beta函数参数的自适应选取,实现了基于杜鹃搜索算法的归一化的非完全Beta函数图像增强方法,实际图像增强实验结果表明了该方法的有效性和可行性。
Image enhancement is a key procedure in image processing, generally, the normalized incomplete Beta function enhancement method has good enhancement effect. However, to obtain good parameter of normalized incomplete Beta function method is still the key and difficult problem which is not fully solved, it often requires human intervention or computation is very time-consuming. The cuckoo search algorithm is a newly proposed metaheuristic algorithm, which is with feature of adaptive, self-organizing intelligent; moreover, it has a strong search ability of the optimal solution. Thus, in the paper, cuckoo search algorithm was employed to seek the optimal parameters for normalized incomplete Beta function image enhancement method and a novel normalized incomplete Beta function based image enhancement method optimized with cuckoo search algorithm was put forward, in the end, the actual image enhancement experimental results showed the effectiveness and feasibility of the method.
Image enhancement is a key procedure in image processing, generally, the normalized incomplete Beta function enhancement method has good enhancement effect. However, to obtain good parameter of normalized incomplete Beta function method is still the key and difficult problem which is not fully solved, it often requires human intervention or computation is very time-consuming. The cuckoo search algorithm is a newly proposed metaheuristic algorithm, which is with feature of adaptive, self-organizing intelligent; moreover, it has a strong search ability of the optimal solution. Thus, in the paper, cuckoo search algorithm was employed to seek the optimal parameters for normalized incomplete Beta function image enhancement method and a novel normalized incomplete Beta function based image enhancement method optimized with cuckoo search algorithm was put forward, in the end, the actual image enhancement experimental results showed the effectiveness and feasibility of the method.
引文
[1]GONZALEZ R C,WINTZ P.Digital image processing[J].Prentice Hall International,2001,28(4):484-486.
[2]TUBBS J D.A note on parametric image enhancement[J].Pattern Recognition,1987,20(6):617-621.
[3]黄小荣,张金玉.遗传算法在图像增强中的应用[J].四川兵工学报,2010(6):67-70.HUANG X R,ZHANG J Y.Application of genetic algorithm in image enhancement[J].Sichuan Ordnance Journal,2010(6):67-70.
[4]李林宜,李德仁.粒子群优化算法在遥感图像增强中的应用[J].测绘科学技术学报,2010,27(2):116-119.LI L Y,LI D R.Research on particle swarm optimization in remote sensing image enhancement[J].Journal of Geomatics Science and Technology,2010,27(2):116-119.
[5]周琳,李琳,邵明省.基于混沌蛙跳算法的图像增强处理[J].激光与红外,2012,42(12):1408-1412.ZHOU L,LI L,SHAO M S.Image enhancement based on chaotic leapfrog algorithm[J].Laser&Infrared,2012,42(12):1408-1412.
[6]丁生荣,马苗,郭敏.人工鱼群算法在自适应图像增强中的应用[J].计算机工程与应用,2012,48(2):185-187.DING S R,MA M,GUO M.Application of artificial fish swarm algorithm in self-adaptive image enhancement[J].Computer Engineering and Applications,2012,48(2):185-187.
[7]YANG X S,DEB S.Cuckoo search via Lévy flights[C]∥Proceeding of World Congress on Nature&Biologically Inspired Computing(Na BIC 2009).Coimbatore,India,2009:210-214.
[8]YANG X S,DEB S.Engineering optimization by cuckoo search[J].International Journal of Mathematical Modeling and Numerical Optimization,2010,1(4):330-343.
[9]CIVICIOGLU P,BESDOK E.A conceptual comparison of the cuckoo-search,particle swarm optimization,differential evolution and artificial bee colony algorithms[J].Artificial Intelligence Review,2013,39(4):315-346.
[2]TUBBS J D.A note on parametric image enhancement[J].Pattern Recognition,1987,20(6):617-621.
[3]黄小荣,张金玉.遗传算法在图像增强中的应用[J].四川兵工学报,2010(6):67-70.HUANG X R,ZHANG J Y.Application of genetic algorithm in image enhancement[J].Sichuan Ordnance Journal,2010(6):67-70.
[4]李林宜,李德仁.粒子群优化算法在遥感图像增强中的应用[J].测绘科学技术学报,2010,27(2):116-119.LI L Y,LI D R.Research on particle swarm optimization in remote sensing image enhancement[J].Journal of Geomatics Science and Technology,2010,27(2):116-119.
[5]周琳,李琳,邵明省.基于混沌蛙跳算法的图像增强处理[J].激光与红外,2012,42(12):1408-1412.ZHOU L,LI L,SHAO M S.Image enhancement based on chaotic leapfrog algorithm[J].Laser&Infrared,2012,42(12):1408-1412.
[6]丁生荣,马苗,郭敏.人工鱼群算法在自适应图像增强中的应用[J].计算机工程与应用,2012,48(2):185-187.DING S R,MA M,GUO M.Application of artificial fish swarm algorithm in self-adaptive image enhancement[J].Computer Engineering and Applications,2012,48(2):185-187.
[7]YANG X S,DEB S.Cuckoo search via Lévy flights[C]∥Proceeding of World Congress on Nature&Biologically Inspired Computing(Na BIC 2009).Coimbatore,India,2009:210-214.
[8]YANG X S,DEB S.Engineering optimization by cuckoo search[J].International Journal of Mathematical Modeling and Numerical Optimization,2010,1(4):330-343.
[9]CIVICIOGLU P,BESDOK E.A conceptual comparison of the cuckoo-search,particle swarm optimization,differential evolution and artificial bee colony algorithms[J].Artificial Intelligence Review,2013,39(4):315-346.