报告题目：Generalized variational framework with minimax optimization for parametric blind deconvolution
时 间：2026年4月17日（星期五）09：30
地 点：理工南楼201
主 办：数学与统计学院、分析数学及应用教育部重点实验室、福建省分析数学及应用重点实验室、统计学与人工智能福建省高校重点实验室、福建省应用数学中心（福建师范大学）
参加对象：感兴趣的老师和研究生
报告摘要：Blind deconvolution (BD), which aims to separate unknown colvolved signals, is a fundamental problem in signal processing. Due to the ill-posedness and underdetermination of the convolution system, it is a challenging nonlinear inverse problem. This talk presents the algorithmic studies of parametric BD, which is typically applied to recover images from ad hoc optical modalities. We propose a generalized variational framework for parametric BD with various priors and potential functions. By using the conjugate theory in convex analysis, the framework can be cast into a nonlinear saddle point problem. We employ the recent advances in minimax optimization to solve the parametric BD by the nonlinear primal-dual hybrid gradient method, with all subproblems admitting closed-form solutions. Numerical simulations on synthetic and real datasets demonstrate the compelling performance of the minimax optimization approach for solving parametric BD.
报告人简介：韩德仁，教授，博士生导师，北京航空航天大学数学科学学院院长。研究方向为大规模优化问题、变分不等式问题及其在交通规划、磁共振成像中的应用。获教育部科学研究优秀成果奖二等奖、江苏省科学技术奖、中国运筹学会青年科技奖等奖项；主持国家自然科学基金重点项目、杰出青年基金项目等多项项目。担任中国运筹学会副理事长、算法软件与应用分会理事长；中国工业与应用数学学会常务理事；《数值计算与计算机应用》、《Journal of the Operations Research Society of China》、《Journal of Global Optimization》、《Asia-Pacific Journal of Operational Research》编委。