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Compressed Sensing: Methods, Theory and Applications $95.00
Editors: Jonathon M. Sheppard
Book Description:
Compressed Sensing: Methods, Theory and Applications presents and describes a method of image Arbitrary Sampling and Bounded Spectrum Reconstruction (ASBSR-method) that allows for drawing near the image sampling rate theoretical minimum. This compilation also discusses results of experimental verification of the ASBSR-method and its possible applicability extensions to solving various underdetermined inverse problems such as: color image demosaicing, image in-painting, image reconstruction from their sparsely sampled or decimated projections, image reconstruction from the modulus of its Fourier spectrum, and image reconstruction from its sparse samples in Fourier domain. Following this, the authors examine a novel framework to obtain HR images from CS imaging systems capturing multiple Low Resolution (LR) images of the same scene. The assumption that when an image admits a sparse representation in a transformed domain, a blurred version of it will also be sparse in the transformed domain, allows for the recovery of blurred images from CS observations. This proposed Compressed Sensing Super Resolution (CSSR) approach, combines existing CS reconstruction algorithms with an LR to HR approach based on the use of a new robust sparsity promoting prior based on super Gaussian regularisation. Additionally, several image CS recovery methods are studied. The concept of CS in image processing is introduced and a brief description of an effective CS reconstruction algorithm, called block-based CS with smoothed-projected Landweber reconstruction (BCS-SPL), is presented. Next, an adaptive CS method is presented, which provides a higher recovered image quality with respect to the BCS-SPL algorithm. Then, the concept of prediction and residual reconstruction is extended to the image CS recovery. (Novinka)

Table of Contents:
Preface

Chapter 1. Compressed Sensing, the ASBSR-Method of Image Sampling and Reconstruction, and the Problem of Digital Image Acquisition with the Lowest Possible Sampling Rate
(L. P. Yaroslavsky, Dept. of Physical Electronics, School of Electrical Engineering, Tel Aviv University, Tel Aviv, Israel)

Chapter 2. The Compressed Sensing Super Resolution of Multiple Images
(Wael H. Saafin, PhD, Department of Computer Science, Loughborough University, Loughborough, UK)

Chapter 3. Image Compressed Sensed Recovery Using Saliency-Based Adaptive Sensing and Residual Reconstruction
(Ali Akbari, Maria Trocan, and Bertrand Granado, Institut Superieur d'Electronique de Paris (ISEP), Paris, France, and others)

Index

   Series:
      Electrical Engineering Developments
   Binding: ebook
   Pub. Date: 2018 - 1st quarter
   ISBN: 978-1-53613-083-6
   Status: AN
  
Status Code Description
AN Announcing
FM Formatting
PP Page Proofs
FP Final Production
EP Editorial Production
PR At Prepress
AP At Press
AV Available
  
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