Compressed Sensing with Side Information on the Feasible Region

This book discusses compressive sensing in the presence of side information. Compressive sensing is an emerging technique for efficiently acquiring and reconstructing a signal. Interesting instances of Compressive Sensing (CS) can occur when, apart from sparsity, side information is available about the source signals. The side information can be about the source structure, distribution, etc. Such cases can be viewed as extensions of the classical CS. In these cases we are interested in incorporating the side information to either improve the quality of the source reconstruction or decrease the number of samples required for accurate reconstruction. In this book we assume availability of side information about the feasible region. The main applications investigated are image deblurring for optical imaging, 3D surface reconstruction, and reconstructing spatiotemporally correlated sources. The author shows that the side information can be used to improve the quality of the reconstruction compared to the classic compressive sensing. The book will be of interest to all researchers working on compressive sensing, inverse problems, and image processing.

Autorentext
Mohammad Rostami,
Singapore University of Technology and Design,
20 Dover Drive,
Singapore 138682
e-mail: m2rostam@uwaterloo.ca

Inhalt
Introduction.- Compressed Sensing.- Compressed Sensing with Side Information on Feasible Region.- Application: Image Deblurring for Optical Imaging.- Application: Surface Reconstruction in Gradient Field.- Conclusions and Future Work.