压缩感知在医学图像压缩中的应用  

Compressed sensing applications in medical image compression

作　　者：雷莉霞[1] 张跃进[1] 黄德昌[1] 

机构地区：[1]华东交通大学信息工程学院,江西南昌330013

出　　处：《计算机工程与设计》2014年第11期3898-3902,共5页Computer Engineering and Design

基　　金：2012年江西省科技厅自然科学青年基金项目(20122BAB211040);2012年华东交通大学校级基金项目(12XX02)

摘　　要：调研压缩感知的数学理论基础和常用方法,包括稀疏变换、测量矩阵和重构算法,利用Matlab软件实现压缩感知实验,比较几种测量矩阵的性能,提出双阈值分块正交匹配追踪重构算法。根据图像不同区域信息量的不同,采取分块处理的方法并加入采样阈值,针对不同子图像块采取不同采样率,提高采样效率;加入判断阈值,降低重构效果对采样阈值的依赖。实验结果表明,该方法能够以较低的采样率实现较高的重构精度,使压缩感知在医学图像压缩方面得到了较好应用。The theory of math and common methods of compressed sensing were researched, including the sparsity representa- tion, the measurement matrix and the reconstruction algorithm. The performances of several measurement matrixes were tested, and a new method called partitioning orthogonal matching pursuit with dual thresholds was proposed. Due to the difference in blocks~ information quantities, the information quantity was estimated by using sampling threshold to select different sampling rates for different blocks, which improve the sampling and reconstruction efficiency. To obtain good reconstruction quality, a judging threshold was used to reduce the dependence on the sampling threshold. The experimental results show that this method can reconstruct an image with high accuracy while the sampling rate is low, so that the compressed sensing is well applied to the medical image compression.

关 键 词：压缩感知 医学图像 图像压缩 测量矩阵 重构算法 

分 类 号：TN391.41[电子电信—物理电子学]