内容简介：We consider the sparse phase retrieval problem, that is, recovering an unknown s-sparse signal from the intensity-only measurements. Specically, we focus on the problem of recovering x from the observations that are convoluted with some specfic kernel, it can also be considered as masked Fourier measurements. This model is motivated by real-world applications in optics and communications. If the convolutional kernel is generated by a random Gaussian vector and the number of subsampled measurements is on the order of spolylogn, one can recover x up to a global phase. Here we discuss the behavior of sparse phase retrieval under more realistic measurements, as opposed to independent Gaussian measurements.

报告人：夏羽

报告人简介：杭州师范大学数学学院副教授，毕业于浙江大学数学系 (导师：李松教授)。主要从事信号图像处理中的数学理论和算法研究. 现阶段在应用数学及数学与信息交叉领域发表一系列学术论文，包括 Applied and Computational Harmonic Analysis, Inverse Problems, IEEE Transactions on Information Theory, IEEE Transactions on Signal Processing等。主持国家自然科学基金项目两项。

时  间：2024年11月17日（周日）上午9：30开始

地  点：腾讯会议：554-725-402

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信息科学技术学院

2024年11月14日