报告人:王成祥（重庆师范大学）

日  期:2019年4月27日

时  间：14:30

地  点: 理科楼 LD202

摘  要:Due to the restriction of computed tomography (CT) scanning environment, the acquired projection data may be incomplete for exact CT reconstruction. Though some convex optimization methods, such as total variation minimization based method, can be used for incomplete data reconstruction, the edge of reconstruction image may be partly distorted for limited-angle CT reconstruction. To promote the quality of reconstruction image for limited-angle CT imaging, in this paper, a nonconvex and nonsmooth optimization model was investigated. To solve the model, a variational proximal alternating linearized minimization (VPALM) method based on proximal mapping in a given metric was proposed. The proposed method can avoid computing the inverse of a huge system matrix thus can be used to deal with the larger-scale inverse problems. What’s more, we show that each bounded sequence generated by VPALM globally converges to a critical point based on the Kurdyka–Lojasiewicz property. Real data experiments are used to demonstrate the viability and effectiveness of VPALM method, and the results show that the proposed method outperforms two classical CT reconstruction methods.

报告人简介:王成祥，2016.12在太阳成集团获得理学博士学位，2017.1-2019.1在电子科技大学从事博士后研究工作。2019.2-至今工作于重庆师范大学数学科学学院。主要研究方向：CT图像重建算法，图像处理。现已发表SCI论文15篇，主持国家自然科学基金青年基金，中国博士后面上项目等，参与国家自然科学基金面上项目2项，青年基金1项。

公司联系人:曾 理

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