Multitask classification and reconstruction using extended Turbo approximate message passing

详细信息    查看全文

• 作者：Ying-Gui Wang ; Le Yang ; Ze-Ying Tang ; Yong Gao
• 关键词：Multitask CS ; Task classification ; Signal reconstruction ; Turbo generalized approximate message passing (Turbo ; GAMP) ; State evolution (SE)
• 刊名：Signal, Image and Video Processing
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：11
• 期：2
• 页码：219-226
• 全文大小：
• 刊物类别：Engineering
• 刊物主题：Signal,Image and Speech Processing; Image Processing and Computer Vision; Computer Imaging, Vision, Pattern Recognition and Graphics; Multimedia Information Systems;
• 出版者：Springer London
• ISSN：1863-1711
• 卷排序：11

文摘

Approximate message passing (AMP) is a known compressive sensing (CS) algorithm, owing to it being computationally efficient, and having high performance and a deterministic state evolution (SE) trajectory. Turbo generalized AMP (Turbo-GAMP) was proposed based on AMP, and it was extended to multitask CS with multiple measurement vectors (MMVs). The resulting Turbo-GAMP-MMV can reconstruct multiple structured-sparse signals when they are well correlated. This paper considers the case where the CS tasks belong to various groups and signals from different groups may have weak correlation. We explore the SE property to enhance Turbo-GAMP-MMV for weakly correlated signals. The developed methods first conduct task classification via dividing CS tasks into groups and then reconstruct the original signals from each group jointly. Experiments using synthetic signals and grey images show that the new algorithms outperform several state-of-art benchmark techniques.