报告人：张立平长聘副教授
报告题目：Minimax Concave Penalty based Quaternion Matrix Completion for Color Image Inpainting
报告摘要：In recent years, quaternion matrix has demonstrated impressive results in color image impainting due to its ability to account for the interrelationships between RGB channels as a holistic entity rather than independent components. Most existing quaternion-based methods formulate a quaternion nuclear norm minimization problem. Nonetheless, the nuclear norm is insufficient to accurately approximate the rank function, thus limiting the efficacy of these models to approximate low rank attributes. To address this issue, we propose a novel nonconvex MCP relaxation model, and prove that this model is an exact penalty for the original low-rank model. To establish optimality conditions, we introduce the subdifferential of the composite function of MCP and the singular values of a quaternion matrix via an elegant Fenchel conjugacy formula. We also propose a globally convergent Quaternion Block Coordinate Descent (QBCD) algorithm to solve our model. The effectiveness and superiority of our proposed method are demonstrated by experiments conducted on authentic visual data sets.
报告时间：2023年6月19日，下午13:00-15:30
报告地点：理学楼404会议室
