題目：Low-rank regularization methods for hyperspectral and multispectral image fusion

　　報告人： 張俊

　　時間：2024年12月19日（周四），上午15：00-16：00

　　地點：理學院1-301會議室

　　報告摘要： Recent research has highlighted the effectiveness of nuclear norm in addressing the fusion of Hyperspectral Image (HSI) and Multispectral Image (MSI) in the same scene. However, the standard nuclear norm method fails to differentiate between different singular values during processing, leading to certain limitations and shortcomings in practical applications. To address this issue, this report investigates HSI-MSI fusion methods from two perspectives: matrix decomposition and tensor decomposition: (1) Innovatively introducing the concept of weighted nuclear norm from image denoising to ensure the preservation of critical data components during image fusion. Specifically, a unified framework integrating weighted nuclear norm, sparse prior, and total variation regularization is proposed; (2) To deeply explore the low-rank characteristics of HSI, this report introduces a newly developed HSI-MSI fusion method within the framework of Tensor Ring (TR) decomposition by integrating the TR factor-based logarithmic tensor nuclear norm with weighted TV.

　　報告人簡介：

　　張俊，南昌工程學院理學院副教授，碩士生導師，江西省優秀青年基金獲得者。2013年6月獲湖南大學理學博士學位，并在該校電氣與信息工程學院進行了博士后研究工作。2017.10-2018.10美國德克薩斯大學訪問學者，并于2024年短期訪問香港城市大學。現為“應用統計”碩士專業學位點數據科學方向的負責人，江西省電子學會理事。主要研究方向：高光譜遙感圖像處理，數值最優化，圖像復原與分割。主持在研國自科地區科學基金項目、江西省自然科學基金優秀青年基金項目和面上項目各1項；主持完成國家級、省級科研項目4項。在IEEE TGRS、IEEE JSTARS、SP、AMC、AMM等著名學術期刊上發表學術論文30余篇，獲得授權發明專利1項。