报告题目：Fast minimization for curvature based regularization models based on bilinear decomposition
报告人：常慧宾
工作单位：天津师范大学
报告时间：2025年1月3日上午10:30-12:30.
地点：数信学院305
报告摘要：The curvature based regularization models can generate artifact-free results compared with the traditional total variation regularization model in image processing. However, strong nonlinearity and singularity due to the curvature term pose a great challenge for one to design fast and stable algorithms for the EE model. We propose a new, fast, hybrid alternating minimization (HALM) algorithm based on a bilinear decomposition of the gradient of the underlying image and prove the global convergence of the minimizing sequence generated by the algorithm under mild conditions. A host of numerical experiments are conducted to show that the new algorithm produces good results with much-improved efficiency compared to other state-of-the-art algorithms. As one of the benchmarks, we show that the average running time of the HALM algorithm is at most one-quarter of that of the fast operator-splitting-based Deng-Glowinski-Tai algorithm.
报告人简介：常慧宾，博导，国家级青年人才，天津青年五四奖章获得者。主要研究方向为：反问题建模与计算、图像处理、计算光学、高性能计算等，在本领域重要期刊如SIAM系列、IEEE汇刊等杂志以及学术会议发表学术论文五十余篇，主持4项国家自然科学基金项目。获国际计算科学大会ICCS 2019最佳论文奖、第一届天津市数学和统计学联合年会青年学者奖等。
