Image Reconstruction from Incomplete Data and Its Applications in Experimental Mechanics


Article Preview

In the field of experimental mechanics, there exist some circumstances when only data at the boundary can be obtained while the internal data are unavailable, or when some data are missed due to shadow, illumination saturation and other reasons. Thus it would be helpful if a reasonable estimation of the unavailable or missed data can be obtained. In this study, an algorithm is developed to reconstruct the missed data from the existing ones by generating a series of equations about the missed data and solving for an optimal solution using least-squares approach. Results based on both simulation data and real incomplete experimental data obtained by shearography and fringe projection show the usefulness and potential of the algorithm for experimental mechanics applications.



Key Engineering Materials (Volumes 326-328)

Edited by:

Soon-Bok Lee and Yun-Jae Kim




Y.H. Huang et al., "Image Reconstruction from Incomplete Data and Its Applications in Experimental Mechanics", Key Engineering Materials, Vols. 326-328, pp. 83-86, 2006

Online since:

December 2006




[1] J. Shen, Geometric Bayesian inpainting and applications, In: Image Reconstruction from Incomplete Data II, pp.102-113, Proceeding of SPIE, Washington, (2002).

[2] T. F. Chan and J. Shen, Variational restoration of non-flat image feature: models and algorithms, SIAM J. Appl. Math. 61(4), pp.1338-1361, (2000).

[3] D. Mumford, The Bayesian rationale for energy functions, In: Geometry Driven Diffusion in Computer Vision, pp.141-153, Kluwer Academic, (1994).

[4] Y. Zou and X. C. Pan, Image reconstruction on PI-lines by use of filtered backprojection in helical cone-beam CT, Physics in Medicine and Biology, 49(12), pp.2717-2731, (2004).


[5] W. K. Chooi, S. Matthews, M. J. Bull and S. K. Morcos, Multislice helical CT: the value of multiplanar image reconstruction in assessment of the bronchi and small airways disease, British Journal of Radiology, 76(908), pp.536-540, (2003).


[6] B. P. Sutton, D. C. Noll and J. A. Fessler, Fast iterative image reconstruction for MRI in the presence of field inhomogeneities, IEEE Transactions on Medical Imaging, 22(2), 178-188, (2003).


[7] A. I. Zayed, The radon transform, In: Handbook of Function and Generalized Function Transformations, pp.557-580, CRC Press, (1996).

[8] D. T. Sandwell, Biharmonic spline interpolation of GEOS-3 and SEASAT altimeter data, Geophysical Research Letters, 2, pp.139-142, (1987).


[9] R. J. Gu, J. D. Hovanesian and Y. Y. Hung, Calculations of strains and internal displacement fields using computerized tomography, J. of Applied Mechanics. 58, pp.24-27, (1991).


[10] H. Anton and R. C. Busby, QR-Decomposition; Householder Transformations, In: Contemporary Linear Algebra, pp.417-428, John Wiley & Sons, Inc. 2003. G G G Fig. 5. (a) cracked image (b) reconstructed image (c) 3-D plot of reconstructed image.