基于压缩感知理论的高能闪光照相密度反演方法

Research on the Method for Density Reconstruction Based on Compressed Sensing in High-Energy Flash Radiography

高能闪光照相中需要研究少数投影数据条件下的非轴对称客体的的密度反演问题。现有利用压缩感知思想的全变差TV类算法虽然考虑了图像的局部相似性,但没有考虑图像的非局部相似性。针对上述问题,文中提出了一种基于组稀疏正则化的全变分重建技术TV-GSR。该技术将组稀疏模型集成于TV框架之下,同时考虑了客体图像的局部相似性和非局部自相似性,充分利用了图像的先验稀疏信息,并利用客体的上、下、左、右4点对称性来降低图像重建的规模,重构精度有所增加,重建速度也更快。仿真实验表明,文中提出的TV-GSR算法提升了图像在无噪声和有噪声情况下的重建精度,对于高能闪光图像和纹理细节丰富的CT图像都有较好的效果,具有普适性。

Density reconstruction of non-axisymmetric object from few projected data needs to be studied in high-energy flash radiography.The existing kinds of TV algorithms which exploits the idea of compressed sensing consider the local similarity of images,but they don’t consider the non-local similarity.In view of these problems,this study proposes a total variational reconstruction technique TV-GSR based on group sparse regularization.This technology integrates the group sparse model into the TV framework,and considers the local similarity and non-local self-similarity of the object image,making full use of the prior sparse information of the image.Besides,the proposed technology also uses the four-point symmetry of up,down,left and right of object to reduce the size of image reconstruction.Thus,the reconstruction accuracy increases and the reconstruction speed accelerates.Simulation experiments show that the proposed TV-GSR algorithm improves the reconstruction accuracy of images in noiseless and noisy scenarios,and has good effects on high-energy flash images and CT images with rich texture details,and is universal.