Robust 1-bit Compressive Sensing with Partial Gaussian Circulant Matrices and Generative Priors
2021 IEEE Information Theory Workshop (ITW)(2021)
摘要
In 1-bit compressive sensing, each measurement is quantized to a single bit, namely the sign of a linear function of an unknown vector, and the goal is to accurately recover the vector. While it is most popular to assume a standard Gaussian sensing matrix for 1-bit compressive sensing, using structured sensing matrices such as partial Gaussian circulant matrices is of significant practical importa...
更多查看译文
关键词
Conferences,Computational modeling,Sensors,Compressed sensing,Standards,Optimization,Information theory
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要