Logo - springer
Slogan - springer

Computer Science - Image Processing | Compressed Sensing with Side Information on the Feasible Region

Compressed Sensing with Side Information on the Feasible Region

Rostami, Mohammad

2013, XIII, 69 p. 20 illus.

Available Formats:
eBook
Information

Springer eBooks may be purchased by end-customers only and are sold without copy protection (DRM free). Instead, all eBooks include personalized watermarks. This means you can read the Springer eBooks across numerous devices such as Laptops, eReaders, and tablets.

You can pay for Springer eBooks with Visa, Mastercard, American Express or Paypal.

After the purchase you can directly download the eBook file or read it online in our Springer eBook Reader. Furthermore your eBook will be stored in your MySpringer account. So you can always re-download your eBooks.

 
$29.99

(net) price for USA

ISBN 978-3-319-00366-5

digitally watermarked, no DRM

Included Format: PDF and EPUB

download immediately after purchase


learn more about Springer eBooks

add to marked items

Softcover
Information

Softcover (also known as softback) version.

You can pay for Springer Books with Visa, Mastercard, American Express or Paypal.

Standard shipping is free of charge for individual customers.

 
$39.99

(net) price for USA

ISBN 978-3-319-00365-8

free shipping for individuals worldwide

usually dispatched within 3 to 5 business days


add to marked items

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.

Content Level » Research

Keywords » Compressive Sensing - Derivative Compressive Sampling - Image Deblurring - Inverse Problems - Shack-Hartmann Interferometer - Sparse Sampling

Related subjects » Complexity - Computational Science & Engineering - Image Processing - Signals & Communication

Table of contents 

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.

Popular Content within this publication 

 

Articles

Read this Book on Springerlink

Services for this book

New Book Alert

Get alerted on new Springer publications in the subject area of Computer Imaging, Vision, Pattern Recognition and Graphics.