模糊图像高维空间几何信息自适应复原方法

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。

HDSGI adaptive restoration of blurred image

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.