Deconvolution of Images and Spectra

Deconvolution of Images and Spectra

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Deconvolution is a technique in signal or image processing that is applied when data is difficult to read due to spreading and blurring of corrupt images and experimental results. Through deconvolution, the investigator can gain access to the true and uncorrupted phenomenon. Advantages include reduced noise sensitivity and super resolving capabilities that have lead to important advances such as the explosive development of computer-based communications, neural networks, the discovery of the nucleus of Halley's comet and new insights into cell biology. This second edition addresses both the newest computer hardware applications and the implementation of modern non-linear constrained methods. The text conveys an understanding of the field while providing a selection of effective, practical techniques. The authors assume only a working knowledge of calculus, and emphasizing practical applications over topics of theoretical interest, focusing on areas that have been pivotal to the evolution of the most effective methods.
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Product details

  • Hardback | 460 pages
  • 156.21 x 233.43 x 26.92mm | 938.93g
  • Academic Press Inc
  • San Diego, United States
  • English
  • Revised
  • 2nd Revised edition
  • b&w illustrations
  • 0123802229
  • 9780123802224

Table of contents

Convolution and related concepts, P.A. Jansson; distortion of optical spectra, P.A. Jansson; traditional linear deconvolution methods, P.A. Jansson; modern constrained nonlinear methods, P.A. Jansson; convergence of relaxation algorithms, P.C. Crilly; instrumental considerations, W.E. Blass and G.W. Halsey; deconvolution examples, P.C. Crilly, W.E. Blass and G.W. Halsey; application to electron spectroscopy for chemical analysis, P.A. Jansson and R.D. Davies; decon-volution in optical microscopy, J.R. Swedlow, J.W. Sedat, and D.A. Agard; deconvolution of HST images and spectra, R.J. Hanisch, R.L. White, and R.L. Gilliland; maximum likelihood estimates of spectra, B.R. Frieden; fourier spectrum continuation, S.J. Howard; minimum negativity fourier spectrum continuation, S.J. Howard; alternating projection onto convex sets, R.J. Marks, II.
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Review quote

"This is an excellent practical handbook for using deconvolution with real data for which the positivity contstraint is useful, such as in optical images and spectra...Figures are clear and the text is very readable...We are happy to have this book and recommend it to anyone involved in data analysis or signal processing which involves deconvolution."--AMERICAN SCIENTIST
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