文章摘要如下: We examine the recovery of block sparse signals and extend the framework in
two important directions; one by exploiting signals' intra-block correlation
and the other by generalizing signals' block structure. We propose two families
of algorithms based on the framework of block sparse Bayesian learning (BSBL).
One family, directly derived from the BSBL framework, requires to know the
block structure. Another family, derived from an expanded BSBL framework, is
based on a weaker assumption on the block structure, and can be used in the
case when the block structure is completely unknown. Using these algorithms we
show that exploiting intra-block correlation is very helpful in improving
recovery performance. These algorithms also shed light on how to modify
existing algorithms or design new ones to exploit such correlation to improve
performance.
下面是相关的应用工作,均发表或者接收在IEEE Trans. on Biomedical Engineering:
至此,BSBL的工作就宣告一个段落了。从BSBL算法研究开始到现今,围绕这个框架总共 1,发表/接收了4篇文章到IEEE期刊,分别是IEEE Journal of Selected Topics in Signal Processing, IEEE Trans. on Signal Processing,和IEEE Trans. on Biomedical Engineering 2,发表了若干篇会议文章(CVPR,MICCAI,ICASSP等等) 3,四篇期刊文章在审 4,拿到了工业界5位数的现金奖励(美元)和6位数年薪的工作offer(美元) 5,帮国内的朋友结合某应用领域申请到了30万(RMB)的funding 6,1 US 专利(在审)