报告问题 (Title)：Low-Rank Approximation of Color Image: New Models and Fast Algorithms （彩色图像的低秩迫近新模子和快速算法）

报告人 (Speaker)：贾志刚 教授（江苏师范大学数学与统计学院、数学研究院）

报告时间 (Time)：2024年6月4日(周二) 9:30

报告所在 (Place)：校本部E408

约请人(Inviter)：王卿文 教授

主理部分：理学院数学系

报告摘要：The color video inpainting problem is one of the most challenging problem in the modern imaging science. It aims to recover a color video from a small part of pixels that may contain noise. However, there are less of robust models that can simultaneously preserve the coupling of color channels and the evolution of color video frames. In this talk, we present a new robust quaternion tensor completion model to solve this challenging problem and derive the exact recovery theory. To solve the case without low-rank property, we introduce a new low-rank learning RQTC model, which rearranges similar patches classified by a quaternion learning method into smaller tensors satisfying the prior low-rank assumption.