Optimal Stopping Times For Estimating Bernoulli Parameters With Applications To Active Imaging

2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)(2018)

引用 18|浏览23
暂无评分
摘要
We address the problem of estimating the parameter of a Bernoulli process. This arises in many applications, including photon-efficient active imaging where each illumination period is regarded as a single Bernoulli trial. We introduce a framework within which to minimize the mean-squared error (MSE) subject to an upper bound on the mean number of trials. This optimization has several simple and intuitive properties when the Bernoulli parameter has a beta prior. In addition, by exploiting typical spatial correlation using total variation regularization, we extend the developed framework to a rectangular array of Bernoulli processes representing the pixels in a natural scene. In simulations inspired by realistic active imaging scenarios, we demonstrate a 4.26 dB reduction in MSE due to the adaptive acquisition, as an average over many independent experiments and invariant to a factor of 3.4 variation in trial budget.
更多
查看译文
关键词
adaptive sensing, Bernoulli processes, beta distribution, computational imaging, conjugate prior, low-light imaging, photon counting, total variation regularization
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络