报告题目：Hyperspectral and multispectral image fusion via superpixel-based weighted nuclear norm minimization
报告人：张俊
报告时间：1月4日（周四）13:30-14:30
报告地点：1-301
摘要：Integrating a low-resolution hyperspectral image and a high-resolution multispectral image is widely acknowledged as an effective approach for generating a high-resolution hyperspectral image. Recent studies have highlighted the nuclear norm as an efficient method for this problem through the utilization of low-rankness. However, the standard nuclear norm has a limitation due to treating singular values equally. To address this issue, we have incorporated the concept of the weighted nuclear norm from the image denoising problem into hyperspectral image fusion. Furthermore, we propose a unified framework which integrates the weighted nuclear norm, a sparse prior, and total variation regularization. This framework utilizes the l1 norm of coefficients to promote spatial-spectral sparsity in the fused images, while total variation is employed to preserve the spatial piecewise smooth structure. To efficiently solve the proposed model, we design an alternating direction method of multipliers.
报告人简介：
张俊，南昌工程学院777大象传媒视频入口副教授，硕士生导师。2013年取得湖南大学理学博士学位。2017.10-2018.10美国德克萨斯大学访问学者。江西省电子学会理事。研究兴趣：高光谱遥感图像处理，数值最优化，图像复原与分割。主持完成国家自然科学基金数学天元基金，江西省自然科学基金青年项目和江西省教育厅科技项目各1项；主持在研中国博士后科学基金面上资助项目和江西省自然科学基金面上项目各1项。在IEEE TGRS、SP、AMC、IPI等著名学术期刊上发表学术论文近30篇。
