模糊图像高维空间几何信息自适应复原方法  

HDSGI adaptive restoration of blurred image

作　　者：李庆武[1,2] 张伟[1] 周妍[1,2] 霍冠英[1,2] 盛惠兴[1] 

机构地区：[1]河海大学物联网工程学院,江苏常州213022 [2]常州市传感网与环境感知重点实验室,江苏常州213022

出　　处：《系统工程与电子技术》2014年第12期2538-2542,共5页Systems Engineering and Electronics

基　　金：国家自然科学基金(60972101;41306089);江苏省科技支撑计划(BE2012096;BE2013372);常州市科技支撑计划(CE20135041)资助课题

摘　　要：针对模糊图像高维空间几何信息(high-dimensional space geometrical informatics,HDSGI)复原方法不能自动调节参数的问题,提出一种结合混沌粒子群优化(chaotic particle swarm optimization,CPSO)算法进行模糊图像自适应复原的新方法。HDSGI图像复原算法可以获得清晰的复原图像,但是需要人工调节表征分布曲线的参数,参数选择不合适时复原图像中会出现噪声。将能同时度量图像模糊程度和噪声水平的无参考型图像质量评价指标作为CPSO算法的适应度函数,达到自适应地选择最佳分布曲线的目的,从而可以获得清晰复原图像。复原后的图像的主观视觉评价和定量评价指标均证明了方法的实用性和有效性。For the problem that the high-dimensional space geometrical informatics （HDSGI）blurred image restoration method fails to adjust the parameters automatically,a new blurred image restoration method which combines the HDSGI theory with the chaotic particle swarm optimization （CPSO）algorithm is proposed.Based on the HDSGI theory,the clear restored image can be obtained,while the parameters of the distribution curve in the above method need to be regulated manually and the restored image may result in noise with inappropriate parameters.In this paper,a no-reference quality assessment method,which can measure both noise levels and blurred degrees of images,is adopted as the fitness function of the CPSO algorithm to find the best distribution curve automatically,thus the best restored image is obtained.The subjective vision assessment and the objective quantitative assessment of images demonstrate that the proposed method is practical and effective.

关 键 词：图像复原 高维空间几何信息学 粒子群优化算法 图像质量评价 HIGH-DIMENSIONAL space GEOMETRICAL informatics (HDSGI) particle SWARM optimization (PSO) image quality assessment (IQA) 

分 类 号：TP394.1[自动化与计算机技术—计算机应用技术]