论文标题
一种非凸离网稀疏尖峰估计的算法，并具有最小的分离约束
An algorithm for non-convex off-the-grid sparse spike estimation with a minimum separation constraint
论文作者
论文摘要
理论结果表明，在最小的分离假设下，可以从（可能是压缩的）傅立叶测量中估算稀疏的离线尖峰。我们提出了一种实用算法，以最大程度地减少基于投影梯度下降和初始化过程的梯度下降的相应非凸功能。我们对算法的理论基础给出了定性见解，并提供了显示其成像问题潜力的实验。
Theoretical results show that sparse off-the-grid spikes can be estimated from (possibly compressive) Fourier measurements under a minimum separation assumption. We propose a practical algorithm to minimize the corresponding non-convex functional based on a projected gradient descent coupled with an initialization procedure. We give qualitative insights on the theoretical foundations of the algorithm and provide experiments showing its potential for imaging problems.
