内容簡介：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.

報告人：夏羽

報告人簡介：杭州師範大學數學學院副教授，畢業于浙江大學數學系 (導師：李松教授)。主要從事信号圖像處理中的數學理論和算法研究. 現階段在應用數學及數學與信息交叉領域發表一系列學術論文，包括 Applied and Computational Harmonic Analysis, Inverse Problems, IEEE Transactions on Information Theory, IEEE Transactions on Signal Processing等。主持國家自然科學基金項目兩項。

時  間：2024年11月17日（周日）上午9：30開始

地  點：騰訊會議：554-725-402

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