摘要
在对图像数据进行识别和恢复的过程中,由于图像的相似性,存在数据的稀疏性。在压缩感知恢复图像的过程中,由于缺乏对统计数据先验信息的利用,导致计算复杂度高,并且恢复精度低。针对此问题,采用压缩感知的改进算法对图像进行恢复,对矩阵的相似性和相似距离进行定义,根据定义应用主成分分析映射以及贝叶斯先验信息对图像的迭代恢复算法进行改进。实验结果显示,所提方法的准确性明显高于其他恢复算法,并且恢复的图像清晰度高。根据计算复杂度的对比,所提算法的计算复杂度低,计算时间少。
In the process of recognizing and restoring image data,data＇s sparsity usually occurs due to the similarity of images.In the process of compressed sensing image restoration,the lack of prior information on statistical data generally brings about higher computational complexity and lower restoring accuracy.This paper introduced a refined algorithm of compressed sensing to restore the images,and it defined similarity distances of the matrix as well.The similarity distances and similarity of the matrix are defined by the similarity of the image.Based on the present definition,the application of principal component analysis mapping and Bayesian prior information will enhance images＇ iterative recovery algorithm.Experimental results show that the proposed method is more accurate than other restoration algorithms and the images restored are of better definition.Comparatively speaking,the proposed algorithm has a lower computational complexity and consumes a shorter period of computational time.
出处
《计算机科学》
CSCD
北大核心
2017年第6期312-316,共5页
Computer Science
基金
广西高校科学技术研究项目(KY2015LX300
KY2015YB241
2013LX112)
玉林市科技创业成果转化专项经费项目(16022009)资助
关键词
压缩感知
主成分映射
图像恢复
计算复杂度
Compressed sensing
Principal component mapping
Image restoration
Computational complexity
