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Statistical Image Processing Techniques for Noisy Images: An Application-Oriented ApproachMarch 2004
Publisher:
  • Plenum Publishing Co.
ISBN:978-0-306-47865-9
Published:01 March 2004
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Bibliometrics
Contributors
  • Paris-Saclay University
  • Institut Fresnel

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Reviews

Vladimir Botchev

This is perhaps the first time that essential statistical image processing techniques are collected in a dedicated book, rather than presented (and thus buried) in a much larger image processing context, as a chapter or specialized book section. Image processing practitioners shall, without a doubt, place in high consideration such a valuable addition to the existing image processing literature. Chapter 2 is the real start of the book, since only general introductory remarks are to be found in the first chapter. This second chapter presents a concise and solid treatment of both statistical signal processing as applied in general, in the context of linear filters, and the image processing case. Very well illustrated, the chapter discusses filter design, stability, and regularization techniques for stabilizing. An appendix on the Lagrangian multipliers constrained optimization technique is also included. The next two chapters introduce statistical correlation techniques, and their applicability to different types of noise. A solid background is presented on hypothesis testing and optimal filtering, among others. The application chapter discusses object location and edges extraction. The next two chapters address segmentation via the statistical snake approach, and the application of a polygonal statistical snake. These two chapters offer a very insightful discussion on segmentation, and provide algorithms for contour determination and contour coding. The final chapter of the book presents an application example: the processing of coherent polarimetric images. Acknowledging the fact that there will be readers who are not familiar with this type of imaging, the authors provide a few essential tutorial sections, and overall this chapter has a tutorial flavor. An extensive reference section concludes the book. This book is a good example of an application-oriented statistical signal processing text, is very well illustrated, and could be recommended as a supplementary text in a statistical signal processing course. Online Computing Reviews Service

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