讲座人简介： 兰州大学数学与统计学院教授，博导。2008年在香港浸会大学获得博士学位。研究兴趣包括数值线性代数和图像复原，已发表论文40余篇，发表期刊包括SIAM Journal on Imaging Sciences, IEEE transactions on Image Processing and SIAM Journal on Scientific Computing等。以第一完成人获得2021年甘肃省自然科学二等奖。

讲座简介： Photoacoustic tomography (PAT) is a new biomedical imaging modality. It has great advantages in early diagnosis of human disease and accurate monitoring of disease progression. In photoacoustic imaging, when a beam of short-pulsed laser illuminates the biological tissue, the photoacoustic effect leads to the emergence of the acoustic waves in the tissue. The initial acoustic pressure in the tissue reveals the structures of the tissue. The purpose of PAT reconstruction problem is to obtain the initial acoustic pressure in the tissue from the collected photoacoustic signal information. In this talk, we propose a rank minimization-based regularization model for sparse-view photoacoustic image reconstruction problem. We design a proximal alternating iterative algorithm to solve the model and the convergence of the algorithm is demonstrated by utilizing the Kudyka-Łojasiewicz theory. The experimental results show that the proposed method is competitive with the existing state-of-the-art PAT reconstruction methods in terms of both reconstructed quantities and visual effects for sparse-view PAT reconstruction problem.