强激光与粒子束, 2016, 28(9): 094002, 网络出版: 2016-09-01
Constrained optimization reconstruction for flash radiographic image
中国工程物理研究院 流体物理研究所, 四川 绵阳 621900
图像重建 闪光照相 广义变分 约束优化 image reconstruction flash X-ray radiography generalized variation constrained optimization
针对闪光照相图像受模糊及噪声影响的问题, 提出了一种基于约束优化的闪光照相图像重建算法。该算法建立基于平行束投影的正向成像矩阵, 并通过嵌入模糊矩阵表达成像过程中的模糊因素, 采用最速下降法求解重建问题。在算法中设计了预优矩阵以提高迭代重建速度, 利用客体密度值非负、密度分布分段光滑并含有阶跃性边界的先验知识,设计和采用了非负约束、光滑约束及广义变分边界约束条件。对仿真FTO客体图像及实际闪光照相图像的重建结果表明, 基于约束优化的重建算法具有良好的边界保持能力及噪声抑制能力, 可以有效提高图像重建质量。
Blurring and noise are serious problems in hydro-test with high-energy X-ray radiography and make it difficult to reconstruct density distributions from radiographic images. A constrained optimization reconstruction method to decrease the blurring and noise impact is proposed. In this method, the parallel-beam X-ray projections are modeled by inserting a blurring matrix in. The optimization reconstruction problem is minimized by steepest descent method, and a preconditioned matrix has been adopted to improve the reconstruction efficiency. We focus on the tomographic reconstruction of piecewise smooth objects involving sharp edges, so the algorithm is based on generalized-variation-minimization constraints, piecewise constraints and the non-negative density values constraints. We applied the reconstruction algorithm to reconstruct computer-synthesized images of the French Test Object (FTO) and a hydro-test object image. The results show that our method is beneficial to improve the quality of reconstructed image with better performance of noise smoothing and better edge preserving as well.