Compressed Sensing with Side Information on the Feasible Region [electronic resource] / by Mohammad Rostami.Material type: TextLanguage: English Series: SpringerBriefs in Electrical and Computer Engineering: Publisher: Heidelberg : Springer International Publishing : Imprint: Springer, 2013Description: XIII, 69 p. 20 illus. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783319003665Subject(s): Computer science | Computer vision | Computer Science | Computer Imaging, Vision, Pattern Recognition and Graphics | Signal, Image and Speech Processing | Statistical Physics, Dynamical Systems and Complexity | Computational Science and EngineeringAdditional physical formats: Printed edition:: No titleDDC classification: 006.6 LOC classification: T385TA1637-1638TK7882.P3Online resources: Click here to access online
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.
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.