报 告 人：武婷婷 教授
报告题目：Blind Image Deconvolution: New Approaches
报告时间：2024年12月20日（周五）下午3:00
报告地点：静远楼204学术报告厅
主办单位：数学与统计学院、数学研究院、科学技术研究院
报告人简介：
武婷婷教授，博士生导师，曾获得江苏高校“青蓝工程”优秀青年骨干教师、江苏省“科技副总”、南京邮电大学1311人才计划“鼎新学者”等称号。目前是中国工业与应用数学学会会员、江苏省运筹学会会员、中国数学会会员，担任JMIV, JIMO等杂志及会议审稿人。主要从事优化算法及其在信息科学、统计等领域中的应用研究，目前发表国内外知名期刊论文65篇，研究成果发表于《SIAM Journal on Imaging Sciences》、《Journal of Scientific Computing》以及《IEEE Transactions on Neural Networks and Learning Systems》等国际权威期刊上。
报告摘要：
Blind image deblurring is challenging task in image restoration. In deblurring, accurate blur kernel estimation in the first stage is crucial, but errors often lead to artifacts. A robust model is proposed that decomposes the blur kernel into deterministic and random components, improving estimation through iterative refinement and utilizing the Lp-norm for better restoration. In addition, a fractional-order variational model is introduced to reduce ringing artifacts, while the Patch-wise Minimal Pixels prior distinguishes between clean and blurred image blocks. These approaches improves restoration quality and has proven convergence, demonstrating superior performance in blind image deblurring.
