图像字典的盲构造算法  

Image dictionary construction via blind algorithm

作　　者：李洪均[1] 谢正光[1] 李蕴华[1] 王伟[1] 

机构地区：[1]南通大学电子信息学院,江苏南通226019

出　　处：《光电子．激光》2013年第9期1825-1831,共7页Journal of Optoelectronics·Laser

基　　金：国家自然科学基金(61171077);交通部应用基础研究(2011-319-813-510);江苏省高校自然科学研究(12KJB510025;12KJB510026);南通市引进人才项目(03080415);南通大学创新人才基金(2009)资助项目

摘　　要：为了使冗余字典能够自适应地表征图像特征,提出了一种优化的图像字典构造算法。算法采用了冗余字典内基元类之间的灰色关联度作为字典优化依据,建立了一种新的字典优化原则,提出了一种自适应的字典设计算法。算法能够根据图像结构和噪声等信息自适应地选择字典的冗余因子,将算法运用于图像去噪,结果表明,算法效率大大提高,同时也提高了图像去噪效果。Traditional redundant dictionary can not represent the image features self-adaptively. In order to overcome this drawback, this paper presents an optimal image dictionary construction algorithm. The algorithm establishes a novel optimal principle, using grey relational theory to analyze the value in a redundant dictionary. First we choose grey relation to express the relation between atoms and their characters in dictionary. The value of grey relation of atoms can present their characteristics and measure their relations,and it can be the criteria to realign the atoms in dictionary. Then we optimize the arrays of atoms according to the value of grey relation and it can express different characteristics in different areas of an image. Finally,we construct an adaptive dictionary which accords with the local and whole characteristics in image by the model of grey relational value and noise. The new method provides an efficient way to measure the relation between atoms in image dictionary and a better way to express image structure characteristics. It can select redundant factors of dictionary adaptively according to the image structure and noise information. The experimental results show that the proposed algorithm enhances the dictionary recovery ability significantly and improves the efficiency and the denoising effect greatly.

关 键 词：图像稀疏表示 字典优化 灰色关联度 

分 类 号：TN911.73[电子电信—通信与信息系统]