Skip to main content
SpringerLink
Log in

Wavelets and Multiscale Transform in Astronomical Image Processing

  • Chapter
Handbook of Massive Data Sets

Part of the book series: Massive Computing ((MACO,volume 4))

Abstract

With the requirements of scientific and medical image database support in mind, we describe a range of useful technologies for storage, transmission and display. These new technologies are all based on discrete wavelet or related multiscale transforms. Other important issues include noise modeling, and the innovative use of entropy for information characterization.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 629.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 799.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 799.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Bibliography

  • M. Antonini, M. Barlaud, P. Mathieu, and I. Daubechies: Image coding using wavelet transform. IEEE Transactions on Image Processing, 1: 205–220, 1992.

    Article  Google Scholar 

  • A. Bijaoui, F. Rué, and B. Vandame: Multiscale vision and its application to astronomy. In V. Di Gesù, M.J.B. Duff, A. Heck, M. C. Maccarone, L. Scarsi, and H.U. Zimmermann, editors, Data Analysis in Astronomy, pages 337–343. World Scientific, 1997.

    Google Scholar 

  • A. Bijaoui, J.L. Starck, and F. Murtagh: Restauration des images multi-échelles par l’algorithme à trous. Traitement du Signal, 11: 229–243, 1994.

    MATH  Google Scholar 

  • Y. Bobichon and A. Bijaoui: A regularized image restoration algorithm for lossy compression in astronomy. Experimental Astronomy, 7: 239–255, 1997.

    Article  Google Scholar 

  • F. Bonnarel, P. Fernique, F. Genova, J.G. Bartlett, O. Bienaymé, J. Florsch, and H. Ziaeepour: Aladin: a reference tool for identification of astronomical sources. In D.M. Mehringer, R.L. Plante, and D.A. Roberts, editors, Astronomical Data Analysis Software and System VIII, pages 229–232. Astronomical Society of the Pacific, 1999.

    Google Scholar 

  • P. J. Burt and A. E. Adelson: The laplacian pyramid as a compact image code. IEEE Transactions on Communications, 31: 532–540, 1983.

    Article  Google Scholar 

  • E. C. Chang: Foveation Techniques and Scheduling Issues in Thinwire Visualization. PhD thesis, Department of Computer Science, New York University, 1998.

    Google Scholar 

  • E. C. Chang and C. K. Yap: A wavelet approach to foveating images. In Proc. 13th ACM Symp. Computational Geometry, pages 397–399. ACM, 1997. Extended version at: ftp://cs.nyu.edu/pub/local/yap/visual/foveated.ps.gz.

    Google Scholar 

  • E. C. Chang, C. K. Yap, and T. J. Yen: Realtime visualization of large images over a thinwire. In Proc. IEEE Visualization, 1997. Available at: http://www.cz3.nus.edu.sg/—changec/pub.html.

    Google Scholar 

  • R. Clausius: Annalen der physik, serie 2, 1865.

    Google Scholar 

  • A. Cohen, I. Daubechies, and J.C. Feauveau: Biorthogonal bases of compactly supported wavelets. Communications in Pure and Applied Mathematics, 45: 485–560, 1992.

    Article  MathSciNet  MATH  Google Scholar 

  • I. Daubechies: Orthogonal bases of compactly supported wavelets. Communications in Pure and Applied Mathematics, 41: 909–996, 1988.

    Article  MATH  Google Scholar 

  • J. C. Feauveau: Analyse multirésolution par ondelettes non-orthogonales et bancs de filtres numériques. PhD thesis, Université Paris Sud, 1990.

    Google Scholar 

  • G. W. Furnas: Generalized fisheye views. In Proc. ACM CHI’86 Conf. on Human Factors in Computing Systems, pages 16–32, 1986.

    Google Scholar 

  • S. C. Hirtle: The cognitive atlas: using gis as a metaphor for memory. In M. J. Egenhofer and R. G. Golledge, editors, Spatial and Temporal Reasoning in Geographic Information Systems, pages 263–271. Oxford University Press, 1998.

    Google Scholar 

  • M. Holschneider, R. K. Martinet, J. Morlet, and P. Tchamitchian: A real-time algorithm for signal analysis with the help of the wavelet transform. In J. M. Combes, A. Grossmann, and Ph. Tcha-mitchian, editors, Wavelets: Time-Frequency Methods and Phase-Space, pages 286–297. Springer-Verlag, Berlin, 1989.

    Chapter  Google Scholar 

  • E. T. Jaynes: Information theory and statistical mechanics. Phys. Rev., 106: 620–630, 1957.

    Article  MathSciNet  MATH  Google Scholar 

  • M. Louys, J. L. Starck, S. Mei, F. Bonnarel, and F. Murtagh: Astronomical image compression. Astronomy and Astrophysics Supplement, 136: 579–590, 1999.

    Article  Google Scholar 

  • S. Mallat: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11: 674–693, 1989.

    Google Scholar 

  • MR/1.: The multiresolution analysis software, version 2.0, and mr/2: Multiresolution entropy, version 1. 0, 1999. http://www.multiresolution.com.

  • F. Murtagh, J. L. Starck, and M. Louys: Very high quality image compression based on noise modeling. International Journal of Imaging Systems and Technology, 9: 38–45, 1998.

    Article  Google Scholar 

  • F. Murtagh, J.L. Starck, and A. Bijaoui: Multiresolution in astronomical image processing: a general framework. The International Journal of Image Systems and Technology, 6: 332–338, 1995.

    Article  Google Scholar 

  • A. Poulakidas, A. Srinivasan, O. Egecioglu, O. Ibarra, and T. Yang: Experimental studies on a compact storage scheme for wavelet-based multiresolution subregion retrieval. In Proc. NASA 1996 Combined Industry, Space and Earth Science Data Compression Workshop, Utah, April 1996. Linked from: http: //pw 1. netcom. com/’tmhenry/thePaper/ Sec t3. html.

    Google Scholar 

  • W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery: Numerical Recipes: The Art of Scientific Computing. Cambridge University Press, 2 edition, 1992.

    Google Scholar 

  • M Sakar and M.H. Brown: Graphical fisheye views of graphs. In Proc. CHI’92, pages 83–91, Monterey, CA, 1992.

    Google Scholar 

  • C. E. Shannon: A mathematical theory for communication. Bell Systems Technical Journal, 27: 379–423, 1948.

    Article  MathSciNet  MATH  Google Scholar 

  • J. M. Shapiro: Embedded image coding using zerotrees of wavelet coefficients. IEEE Transactions on Signal Processing, 41: 3445–3462, 1993.

    Article  MATH  Google Scholar 

  • J. L. Starck and F. Murtagh: Multiscale entropy filtering. Signal Processing, 76: 147–165, 1999.

    Article  MATH  Google Scholar 

  • J. L. Starck, F. Murtagh, and A. Bijaoui: Image and Data Analysis: The Multiscale Approach. Cambridge University Press, 1998a.

    Google Scholar 

  • J. L. Starck, F. Murtagh, and F. Bonnarel: Multiscale entropy for semantic description of images and signals. submitted, 2000.

    Google Scholar 

  • J. L. Starck, F. Murtagh, B. Pirenne, and M. Albrecht: Astronomical image compression based on noise suppression. Publications of the Astronomical Society of the Pacific, 108: 446–455, 1996.

    Article  Google Scholar 

  • J.L. Starck, F. Murtagh, and R. Gastaud: A new entropy measure based on the wavelet transform and noise modeling. IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing, 45: 1118–1124, 1998b.

    Article  MATH  Google Scholar 

  • M.C. Stone, K. Fishkin, and E.A. Bier: The movable filter as a user interface tool. In Proc. CHI’94 Conf. Human Factors in Comput. Syst., pages 306–312, 1994.

    Google Scholar 

  • O. Stromme: On the applicability of wavelet transforms to image and video compression. PhD thesis, University of Strathclyde, February 1999.

    Google Scholar 

  • W. Sweldens: The lifting scheme: a custom-design construction of biorthogonal wavelets. Appl. Comput. Harmon. Anal., 3: 186–200, 1996.

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science, The Queen’s University of Belfast, Belfast, BT7 1NN, Northern Ireland

    Fionn Murtagh

  2. DAPNIA/SEI-SAP, CEA/Saclay, 91191, Gif sur Yvette, France

    Jean-Luc Starck

Authors
  1. Fionn Murtagh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jean-Luc Starck
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. AT&T Labs Research, USA

    James Abello  & Mauricio G. C. Resende  & 

  2. University of Florida, USA

    Panos M. Pardalos

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer Science+Business Media Dordrecht

About this chapter

Cite this chapter

Murtagh, F., Starck, JL. (2002). Wavelets and Multiscale Transform in Astronomical Image Processing. In: Abello, J., Pardalos, P.M., Resende, M.G.C. (eds) Handbook of Massive Data Sets. Massive Computing, vol 4. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-0005-6_13

Download citation

  • DOI: https://doi.org/10.1007/978-1-4615-0005-6_13

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-4882-5

  • Online ISBN: 978-1-4615-0005-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation