Investigation On Radiographic Image Reconstruction Based On CCG

The flash X-ray radiography is one of main methods to detect primary nuclear weapon. Because the flash X-ray radiography system is complicated, there is a large numbers of noises in the projection image. To get reconstruction data of high precision, we should study a CT(Computerized tomography)algorithm which is good at noise removal.Base on image reconstruction theory, flash X-ray radiography image reconstruction model is discussed in this paper. For the single view flash X-ray radiography, the author adopts concentric circles gridding method to section segmentation and consummates its corresponding reconstruction equation. In researching on iterative image reconstruction, reconstruction quality and convergence analysis between CG (Conjugate Gradient) and CCG (Constrained Conjugate Gradient) which includes nonnegative and smoothness constraint condition, have been concluded. New preconditioned method to reconstruction equation of the single view flash X-ray radiography is consummated. The reconstruction problem is ill-posed, so two optimal criterions, the least module and the smoothness criterion base on Tikhonov regularization technique, are introduced into reconstruction algorithm. Many regularization parameters choice strategies are investigated, and the TPA(Two—Parameter Algorithm) strategy which is based on the Morozov discrepancy principles, is implemented in two regularization reconstruction algorithms.Numerical experiment results show that the nonnegative and smoothness constraint condition can overcome the difficulty of iteration semiconvergent, preconditioned technique can improve convergence rate and reconstruction accuracy, smoothness regularization criterion can meliorate ill-posed problem of reconstruction and enhance iteration stability, and the TPA is an effective strategy of regularization parameters choice. Compared with the FBP algorithm, the image reconstruction algorithm based on CCG is more efficient in reconstruction quality, image smoothness, boundary maintenance and noise removal.