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

　　報(bào)告人： 張俊

　　時(shí)間：2024年12月19日（周四），上午15：00-16：00

　　地點(diǎn)：理學(xué)院1-301會議室

　　報(bào)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(bào)告人簡介：

　　張俊，南昌工程學(xué)院理學(xué)院副教授，碩士生導(dǎo)師，江西省優(yōu)秀青年基金獲得者。2013年6月獲湖南大學(xué)理學(xué)博士學(xué)位，并在該校電氣與信息工程學(xué)院進(jìn)行了博士后研究工作。2017.10-2018.10美國德克薩斯大學(xué)訪問學(xué)者，并于2024年短期訪問香港城市大學(xué)。現(xiàn)為“應(yīng)用統(tǒng)計(jì)”碩士專業(yè)學(xué)位點(diǎn)數(shù)據(jù)科學(xué)方向的負(fù)責(zé)人，江西省電子學(xué)會理事。主要研究方向：高光譜遙感圖像處理，數(shù)值最優(yōu)化，圖像復(fù)原與分割。主持在研國自科地區(qū)科學(xué)基金項(xiàng)目、江西省自然科學(xué)基金優(yōu)秀青年基金項(xiàng)目和面上項(xiàng)目各1項(xiàng)；主持完成國家級、省級科研項(xiàng)目4項(xiàng)。在IEEE TGRS、IEEE JSTARS、SP、AMC、AMM等著名學(xué)術(shù)期刊上發(fā)表學(xué)術(shù)論文30余篇，獲得授權(quán)發(fā)明專利1項(xiàng)。