OSA's Digital Library

Virtual Journal for Biomedical Optics

Virtual Journal for Biomedical Optics


  • Editors: Andrew Dunn and Anthony Durkin
  • Vol. 8, Iss. 7 — Aug. 1, 2013

Design of an image restoration algorithm for the TOMBO imaging system

Shachar Mendelowitz, Iftach Klapp, and David Mendlovic  »View Author Affiliations

JOSA A, Vol. 30, Issue 6, pp. 1193-1204 (2013)

View Full Text Article

Enhanced HTML    Acrobat PDF (1877 KB)

Browse Journals / Lookup Meetings

Browse by Journal and Year


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools



The TOMBO system (thin observation module by bound optics) is a multichannel subimaging system over a single electronic imaging device. Each subsystem provides a low-resolution (LR) image from a unique lateral point of view. By estimating the image’s lateral position, a high-resolution (HR) image is restored from the series of the LR images. This paper proposes an multistage algorithm comprised of successive stages, improving difficulties in previous suggested schemes. First, the registration algorithm estimates the subchannel shift parameters and eliminates bias. Second, we introduce a fast image fusion, overcoming visual blockiness artifacts that characterized previously suggested schemes. The algorithm fuses the set of sampled subchannel images into a single image, providing the reconstruction initial estimate. Third, an edge-sensitive quadratic upper bound term to the total variation regulator is suggested. The complete algorithm allows the reconstruction of a clean, HR image, in linear computation time, by the use of the linear conjugate gradient optimization. Finally, we present a simulated comparison between the proposed method and a previously suggested image restoration method. The results show that the proposed method yields better reconstruction fidelity while eliminating spatial speckle artifacts associated with the previously suggested method.

© 2013 Optical Society of America

OCIS Codes
(100.3190) Image processing : Inverse problems
(100.6640) Image processing : Superresolution
(110.1758) Imaging systems : Computational imaging
(110.3010) Imaging systems : Image reconstruction techniques

ToC Category:
Imaging Systems

Original Manuscript: November 7, 2012
Revised Manuscript: February 15, 2013
Manuscript Accepted: April 7, 2013
Published: May 23, 2013

Virtual Issues
Vol. 8, Iss. 7 Virtual Journal for Biomedical Optics

Shachar Mendelowitz, Iftach Klapp, and David Mendlovic, "Design of an image restoration algorithm for the TOMBO imaging system," J. Opt. Soc. Am. A 30, 1193-1204 (2013)

Sort:  Author  |  Year  |  Journal  |  Reset  


  1. K. Nitta, R. Shogenji, S. Miyatake, and J. Tanida, “Image reconstruction for thin observation module by bound optics by using the iterative back projection method,” Appl. Opt. 45, 2893–2900 (2006). [CrossRef]
  2. J. Tanida, T. Kumagai, K. Yamada, S. Miyatake, K. Ishida, T. Morimoto, N. Kodou, D. Miyazaki, and Y. Ichioka, “Thin observation module by bound optics (TOMBO): concept and experimental verification,” Appl. Opt. 40, 1806–1813 (2001). [CrossRef]
  3. Y. Kitamura, R. Shogenji, K. Yamada, S. Miyatake, M. Miyamoto, T. Morimoto, Y. Masaki, N. Kondou, D. Miyazaki, J. Tanida, and Y. Ichioka, “Reconstruction of a high-resolution image on a compound-eye image-capturing system,” Appl. Opt. 43, 1719–1727 (2004). [CrossRef]
  4. A. V. Kanaev, J. R. Ackerman, E. F. Fleet, and D. A. Scribner, “TOMBO sensor with scene-independent superresolution processing,” Opt. Lett. 32, 2855–2857 (2007). [CrossRef]
  5. M. Shankar, R. Willet, N. Pitslanis, T. Schulz, R. Gibbons, R. T. Kolste, J. Carrier, C. Chen, D. Prather, and D. Brady, “Thin infrared imaging systems through multichannel sampling,” Appl. Opt. 47, B1–B10 (2008). [CrossRef]
  6. T. Q. Pham, “Spatiotonal adaptivity in super-resolution of under-sampled image sequences,” Ph.D. dissertation (aan de Technische Universiteit Delft, 2006).
  7. D. Robinson and P. Milanfar, “Fundamental performance limits in image registration,” IEEE Trans. Image Process 13, 1185–1199 (2004). [CrossRef]
  8. P. Jorge and S. G. Ferreira, “Interpolation and the discrete Papoulis–Gerchberg algorithm,” IEEE Trans. Signal Process. 42, 2596–2606 (1994). [CrossRef]
  9. J. Tanida, R. Shogenji, Y. Kitamura, K. Yamada, M. Miyamoto, and S. Miyamoto, “Color imaging with an integrated compound imaging system,” Opt. Express 11, 2109–2117 (2003). [CrossRef]
  10. G. Gilboa, N. Sochen, and Y. Y. Zeevi, “Texture preserving variational denoising using an adaptive fidelity term,” Presented at the VLSM 2003, Nice, France (2003) 137–144.
  11. S. D. Babacan, R. Molina, and A. K. Katsaggelos, “Variational Bayesian blind deconvolution using a total variation prior,” IEEE Trans. Image Process 18, 12–26 (2009). [CrossRef]
  12. J. M. Bioucas-Dias, M. A. T. Figueiredo, and J. P. Oliveira, “Adaptive total variation image de-convolution: a majorization-minimization approach,” Presented at the European Signal Processing Conference (EUSIPCO 2006) (2006).
  13. Y. C. Eldar, “Uniformly improving the Cramér-Rao bound and maximum-likelihood estimation,” IEEE Trans. Signal Process 54, 2943–2956 (2006). [CrossRef]
  14. O. Christiansen, T. M. Lee, J. Lie, U. Sinha, and T. F. Chan, “Total variation regularization of matrix-valued images,” Int. J. Biomed. Imaging 2007, 27432 (2007). [CrossRef]
  15. T. Q. Pham, L. J. van Vliet, and K. Schutte, “Influence of signal-to-noise ratio and point spread function on limits of superresolution,” Proc. SPIE 5672, 169–180 (2005). [CrossRef]
  16. K. Choi and T. Schulz, “Signal-processing approaches for image-resolution restoration for TOMBO imagery,” Appl. Opt. 47, B104–B116 (2008). [CrossRef]
  17. D. Han and X. Yuan, “A note on the alternating direction method of multipliers,” J. Optim. Theory Appl. 155, 227–238 (2012). [CrossRef]

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