OSA's Digital Library

Optics Express

Optics Express

  • Editor: Andrew M. Weiner
  • Vol. 21, Iss. 20 — Oct. 7, 2013
  • pp: 23307–23323

Robust destriping method with unidirectional total variation and framelet regularization

Yi Chang, Houzhang Fang, Luxin Yan, and Hai Liu  »View Author Affiliations


Optics Express, Vol. 21, Issue 20, pp. 23307-23323 (2013)
http://dx.doi.org/10.1364/OE.21.023307


View Full Text Article

Enhanced HTML    Acrobat PDF (9568 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

Multidetector imaging systems often suffer from the problem of stripe noise and random noise, which greatly degrade the imaging quality. In this paper, we propose a variational destriping method that combines unidirectional total variation and framelet regularization. Total-variation-based regularizations are considered effective in removing different kinds of stripe noise, and framelet regularization can efficiently preserve the detail information. In essence, these two regularizations are complementary to each other. Moreover, the proposed method can also efficiently suppress random noise. The split Bregman iteration method is employed to solve the resulting minimization problem. Comparative results demonstrate that the proposed method significantly outperforms state-of-the-art destriping methods on both qualitative and quantitative assessments.

© 2013 OSA

OCIS Codes
(100.0100) Image processing : Image processing
(100.3020) Image processing : Image reconstruction-restoration

ToC Category:
Image Processing

History
Original Manuscript: August 28, 2013
Revised Manuscript: September 11, 2013
Manuscript Accepted: September 11, 2013
Published: September 24, 2013

Citation
Yi Chang, Houzhang Fang, Luxin Yan, and Hai Liu, "Robust destriping method with unidirectional total variation and framelet regularization," Opt. Express 21, 23307-23323 (2013)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-21-20-23307


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. S.-W. Chen and J. L. Pellequer, “DeStripe: frequency-based algorithm for removing stripe noises from AFM images,” BMC Struct. Biol.11(1), 7–16 (2011). [CrossRef] [PubMed]
  2. A. H. Lettington, S. Tzimopoulou, and M. P. Rollason, “Nonuniformity correction and restoration of passive millimeter-wave images,” Opt. Eng.40(2), 268–274 (2001). [CrossRef]
  3. A. R. Harvey and R. Appleby, “Passive mm-wave imaging from UAVs using aperture synthesis,” J. Aeronautical107, 87–98 (2003).
  4. P. Rakwatin, W. Takeuchi, and Y. Yasuoka, “Stripe noise reduction in MODIS data by combining histogram matching with facet filter,” IEEE Trans. Geosci. Rem. Sens.45(6), 1844–1856 (2007). [CrossRef]
  5. J. Torres and S. O. Infante, “Wavelet analysis for the elimination of striping noise in satellite images,” Opt. Eng.40(7), 1309–1314 (2001). [CrossRef]
  6. J. J. Pan and C. I. Chang, “Destriping of Landsat MSS images by filtering techniques,” Photogramm. Eng. Remote Sensing58, 1417–1423 (1992).
  7. B. Münch, P. Trtik, F. Marone, and M. Stampanoni, “Stripe and ring artifact removal with combined wavelet--Fourier filtering,” Opt. Express17(10), 8567–8591 (2009). [CrossRef] [PubMed]
  8. P. Mather, Computer Processing of Remotely-Sensed Images: An Introduction (Wiley, 2004).
  9. R. Srinivasan, M. Cannon, and J. White, “Landsat data destriping using power filtering,” Opt. Eng.27, 939–943 (1988). [CrossRef]
  10. J. S. Chen, Y. Shao, H. D. Guo, W. M. Wang, and B. Q. Zhu, “Destriping CMODIS data by power filtering,” IEEE Trans. Geosci. Rem. Sens.41(9), 2119–2124 (2003).
  11. F. L. Gadallah, F. Csillag, and E. J. M. Smith, “Destriping multisensor imagery with moment matching,” Int. J. Remote Sens.21(12), 2505–2511 (2000). [CrossRef]
  12. H. F. Shen and L. P. Zhang, “A MAP-based algorithm for destriping and inpainting of remotely sensed images,” IEEE Trans. Geosci. Rem. Sens.47(5), 1492–1502 (2009). [CrossRef]
  13. H. Carfantan and J. Idier, “Statistical linear destriping of satellite-based pushbroom-type images,” IEEE Trans. Geosci. Rem. Sens.48(4), 1860–1871 (2010). [CrossRef]
  14. J. Fehrenbach, P. Weiss, and C. Lorenzo, “Variational algorithms to remove stationary noise: applications to microscopy imaging,” IEEE Trans. Image Process.21(10), 4420–4430 (2012). [CrossRef] [PubMed]
  15. M. Bouali and S. Ladjal, “Toward optimal destriping of MODIS data using a unidirectional variational model,” IEEE Trans. Geosci. Rem. Sens.49(8), 2924–2935 (2011). [CrossRef]
  16. N. Acito, M. Diani, and G. Corsini, “Subspace-based striping noise reduction in hyperspectral images,” IEEE Trans. Geosci. Rem. Sens.49(4), 1325–1342 (2011). [CrossRef]
  17. X. Q. Liu, Y. L. Wang, and Y. Yuan, “Grahp-regularized low-rank representation for destriping of hyperspectral imges,” IEEE Trans. Geosci. Rem. Sens.51(7), 4009–4018 (2013). [CrossRef]
  18. B. Datt, T. R. McVicar, T. G. Van Niel, D. L. B. Jupp, and J. S. Pearlman, “Preprocessing EO-1 Hyperion hyperspectral data to support the application of agricultural indexes,” IEEE Trans. Geosci. Rem. Sens.41(6), 1246–1259 (2003). [CrossRef]
  19. X. X. Xiong, J. Q. Sun, W. Barnes, and V. Salomonson, “Multiyear on-orbit calibration and performance of Terra MODIS reflective solar bands,” IEEE Trans. Geosci. Rem. Sens.45, 879–889 (2007).
  20. L. X. Yan, H. Z. Fang, and S. Zhong, “Blind image deconvolution with spatially adaptive total variation regularization,” Opt. Lett.37(14), 2778–2780 (2012). [CrossRef] [PubMed]
  21. H. Gao and H. K. Zhao, “Multilevel bioluminescence tomography based on radiative transfer equation Part 2: total variation and l1 data fidelity,” Opt. Express18(3), 2894–2912 (2010). [CrossRef] [PubMed]
  22. E. Vera, P. Meza, and S. Torres, “Total variation approach for adaptive nonuniformity correction in focal-plane arrays,” Opt. Lett.36(2), 172–174 (2011). [CrossRef] [PubMed]
  23. M. Freiberger, C. Clason, and H. Scharfetter, “Total variation regularization for nonlinear fluorescence tomography with an augmented Lagrangian splitting approach,” Appl. Opt.49(19), 3741–3747 (2010). [CrossRef] [PubMed]
  24. J. F. Cai, R. H. Chan, and Z. W. Shen, “A framelet-based image inpaiting algorithm,” Appl. Comput. Harmon. Anal.24(2), 131–149 (2008). [CrossRef]
  25. J. F. Cai, B. Dong, S. Osher, and Z. W. Shen, “Image restoration: total variation; wavelet frames; and beyond,” J. Am. Math. Soc.25(4), 1033–1089 (2012). [CrossRef]
  26. J. F. Cai, H. Ji, C. Liu, and Z. W. Shen, “Framelet-based blind motion deblurring from a single image,” IEEE Trans. Image Process.21(2), 562–572 (2012). [CrossRef] [PubMed]
  27. J. F. Cai, Framelet toolbox version 2.02, http://www.math.uiowa.edu/ jiancai/code/SplitBreg_Deblur.zip .
  28. T. Goldstein and S. Osher, “The split bregman method for L1 regularized problems,” SIAM J. Imag. Sci.2(2), 323–343 (2009). [CrossRef]
  29. D. L. Donoho, “De-noising by soft-thresholding,” IEEE Trans. Inf. Theory41(3), 613–627 (1995). [CrossRef]
  30. X. Zhu and P. Milanfar, “Automatic parameter selection for denoising algorithms using a no-reference measure of image content,” IEEE Trans. Image Process.19(12), 3116–3132 (2010). [CrossRef] [PubMed]
  31. D. L. Donoho and I. M. Johnstone, “Ideal spatial adaptation by wavelet shrinkage,” Biometrika81(3), 425–455 (1994). [CrossRef]
  32. L. Holzer, F. Indutnyi, P. H. Gasser, B. Münch, and M. Wegmann, “Three-dimensional analysis of porous BaTiO3 ceramics using FIB nanotomography,” J. Microsc.216(1), 84–95 (2004). [CrossRef] [PubMed]
  33. L. Holzer, P. H. Gasser, A. Kaech, M. Wegmann, A. Zingg, R. Wepf, and B. Muench, “Cryo-FIB-nanotomography for quantitative analysis of particle structures in cement suspensions,” J. Microsc.227(3), 216–228 (2007). [CrossRef] [PubMed]
  34. A. Zingg, L. Holzer, A. Kaech, F. Winnefeld, J. Pakusch, S. Becker, and L. Gauckler, “The microstructure of dispersed and non-dispersed fresh cement pastes-new in-sight by cryo-microscopy,” Cement Concr. Res.38(4), 522–529 (2008). [CrossRef]
  35. H. Liao and M. K. Ng, “Blind deconvolution using generalized cross-validation approach to regularization parameter estimation,” IEEE Trans. Image Process.20(3), 670–680 (2011). [CrossRef] [PubMed]

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.


« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited