Estimation of defocus blurring parameter based on grayscale mean gradient and particle swarm optimization

doi:10.11772/j.issn.1001-9081.2016.04.1111

Journal of Computer Applications ›› 2016, Vol. 36 ›› Issue (4): 1111-1114.DOI: 10.11772/j.issn.1001-9081.2016.04.1111

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Estimation of defocus blurring parameter based on grayscale mean gradient and particle swarm optimization

WU Zhangping¹, LIU Benyong^1,2

1. College of Big Data and Information Engineering, Guizhou University, Guiyang Guizhou 550025, China;
2. Institute of Intelligent Information Processing, Guizhou University, Guiyang Guizhou 550025, China

Received:2015-09-30 Revised:2015-12-21 Online:2016-04-08 Published:2016-04-10
Supported by:
This work is partially supported by the National Natural Science Foundation of China (60862003), the International Cooperation Projects of Science and Technology Department (2009DFR10530).

基于灰度平均梯度和粒子群优化的散焦图像模糊参数估计

吴章平¹, 刘本永^1,2

1. 贵州大学大数据与信息工程学院, 贵阳 550025;
2. 贵州大学智能信息处理研究所, 贵阳 550025

通讯作者: 刘本永
作者简介:吴章平(1992-),男,四川凉山人,硕士研究生,主要研究方向:图像复原、模式识别; 刘本永(1966-),男,贵州兴义人,教授,博士生导师,博士,主要研究方向:数字图像处理、模式识别。
基金资助:
国家自然科学基金资助项目(60862003);科技部国际合作项目(2009DFR10530)。

Abstract

Abstract: For image deblurring application with defocus blurring effect, a parameter estimation method based on Grayscale Mean Gradient (GMG) and Particle Swarm Optimization (PSO) algorithm was proposed to estimate the blurring parameter. First, a group of point spread functions with different blurring radius were randomly generated by PSO algorithm to process a blurred image with Wiener filtering algorithm, then a series of restored images were obtained and the corresponding GMG values were calculated. Secondly, concerning the property that the definition of an image is positively varied with its GMG value, which is shown by experimental results, the GMG values were taken as the fitness function values of the PSO algorithm, then a particle with maximum fitness was found, and the corresponding blurring parameter was taken as the final result of estimation. The experimental results show that the proposed algorithm outperforms spectral estimation method and cepstrum estimation method in estimation accuracy, especially in the case with large blur radius.

Key words: defocused image, blur parameter estimation, Grayscale Mean Gradient (GMG), Particle Swarm Optimization (PSO) algorithm

摘要： 针对散焦模糊图像的复原问题,提出一种基于灰度平均梯度与粒子群优化(PSO)算法相结合的散焦图像模糊参数估计方法。首先,利用PSO算法随机生成一群不同模糊半径的点扩散函数,分别用维纳滤波算法处理模糊图像,得到一系列复原图像,并计算其对应的灰度平均梯度值;然后,利用图像清晰度与图像灰度平均梯度值成正变关系这一特点,以复原图像的灰度平均梯度值作为粒子群算法的适应度函数值,找出使适应度函数最大的粒子所对应的模糊半径作为最后的估计结果。实验结果表明,与频谱估计方法和倒频谱估计方法相比,所提算法能够更精确地估计出模糊参数,尤其是在大尺度模糊半径的情况下,所提算法估计的精度更高。

关键词: 散焦图像, 模糊参数估计, 灰度平均梯度, 粒子群优化算法

CLC Number:

TP391.4

WU Zhangping, LIU Benyong. Estimation of defocus blurring parameter based on grayscale mean gradient and particle swarm optimization[J]. Journal of Computer Applications, 2016, 36(4): 1111-1114.

吴章平, 刘本永. 基于灰度平均梯度和粒子群优化的散焦图像模糊参数估计[J]. 计算机应用, 2016, 36(4): 1111-1114.

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Estimation of defocus blurring parameter based on grayscale mean gradient and particle swarm optimization

基于灰度平均梯度和粒子群优化的散焦图像模糊参数估计

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