Large-Scale Image Deblurring in Java

  • Piotr Wendykier
  • James G. Nagy
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5101)


This paper describes Parallel Spectral Deconvolution (PSD) Java software for image deblurring. A key component of the software, JTransforms, is the first, open source, multithreaded FFT library written in pure Java. Benchmarks show that JTransforms is competitive with current C implementations, including the well-known FFTW package. Image deblurring examples, including performance comparisons with existing software, are also given.


Peri Sine Tral Deblurring Editing 


  1. 1.
    Sarder, P., Nehorai, A.: Deconvolution methods for 3D fluorescence microscopy images. IEEE Signal Proc. Mag., 32–45 (May 2006)Google Scholar
  2. 2.
    Roggemann, M.C., Welsh, B.: Imaging Through Turbulence. CRC Press, Boca Raton (1996)Google Scholar
  3. 3.
    Sechopoulos, I., Suryanarayanan, S., Vedantham, S., D’Orsi, C.J., Karellas, A.: Scatter radiation in digital tomosynthesis of the breast. Med. Phys. 34, 564–576 (2007)CrossRefGoogle Scholar
  4. 4.
    Hansen, P.C., Nagy, J.G., O’Leary, D.P.: Deblurring Images: Matrices, Spectra and Filtering. SIAM (2006)Google Scholar
  5. 5.
    Hansen, P.C.: Rank-deficient and discrete ill-posed problems. SIAM (1997)Google Scholar
  6. 6.
    Vogel, C.R.: Computational Methods for Inverse Problems. SIAM (2002)Google Scholar
  7. 7.
    Wendykier, P.: JTransforms, Parallel Colt, Parallel Spectral Deconvolution (2008),
  8. 8.
    Rasband, W.S.: ImageJ, U. S. National Institutes of Health, Bethesda, Maryland, USA (2008),
  9. 9.
    Frigo, M., Johnson, S.G.: The design and implementation of FFTW3. Proceedings of the IEEE 93(2), 216–231 (2005)CrossRefGoogle Scholar
  10. 10.
    Stewart, G.W.: Matrix Algorithms, Volume 1: Basic Decompositions. SIAM (1998)Google Scholar
  11. 11.
    Gonzalez, R.C., Wintz, P.: 5. Digital Image Processing. Addison-Wesley, Reading (1977)Google Scholar
  12. 12.
    Kilmer, M.E., O’Leary, D.P.: Choosing regularization parameters in iterative methods for ill-posed problems. SIAM J. Matrix Anal. Appl. 22, 1204–1221 (2001)MATHCrossRefMathSciNetGoogle Scholar
  13. 13.
    Doederlein, O.: Mustang’s HotSpot Client gets 58% faster! (2005),
  14. 14.
    Hoschek, W.: Colt Project (2004),
  15. 15.
    Heideman, M.T., Johnson, D.H., Burrus, C.S.: Gauss and the history of the fast Fourier transform. Archive for History of Exact Sciences 34, 265–277 (1985)MATHCrossRefMathSciNetGoogle Scholar
  16. 16.
    Cooley, J.W., Tukey, J.W.: An Algorithm for the Machine Calculation of Complex Fourier Series. Mathematics of Computation 19(90), 297–301 (1965)MATHCrossRefMathSciNetGoogle Scholar
  17. 17.
    Van Loan, C.: Computational Frameworks for the Fast Fourier Transform. SIAM (1992)Google Scholar
  18. 18.
    Johnson, S.G., Frigo, M.: A modified split-radix FFT with fewer arithmetic operations. IEEE Trans. Signal Processing 55(1), 111–119 (2007)CrossRefMathSciNetGoogle Scholar
  19. 19.
    Yavne, R.: An economical method for calculating the discrete Fourier transform. In: AFIPS Fall Joint Computer Conference, pp. 115–125 (1968)Google Scholar
  20. 20.
    Duhamel, P., Hollmann, H.: Split Radix FFT Algorithms. Electronic Letters 20, 14–16 (1984)CrossRefGoogle Scholar
  21. 21.
    Ooura, T.: General Purpose FFT (Fast Fourier/Cosine/Sine Transform) Package (2006),
  22. 22.
    Sun Microsystems: New Features and Enhancements J2SE 5.0 (2004),
  23. 23.
    Linnenbrügger, N.: FFTJ and DeconvolutionJ (2002),
  24. 24.
    NASA: Great Images in NASA. Ed White performs first U.S. spacewalk (1965),
  25. 25.
    Orchard, J.: His Brain (2007),

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Piotr Wendykier
    • 1
  • James G. Nagy
    • 1
  1. 1.Dept. of Math and Computer ScienceEmory UniversityAtlantaUSA

Personalised recommendations