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Issue No.05 - May (2009 vol.31)
pp: 811-823
Justin Domke , University of Maryland, College Park
Yiannis Aloimonos , University of Maryland, College Park
ABSTRACT
Since cameras blur the incoming light during measurement, different images of the same surface do not contain the same information about that surface. Thus, in general, corresponding points in multiple views of a scene have different image intensities. While multiple-view geometry constrains the locations of corresponding points, it does not give relationships between the signals at corresponding locations. This paper offers an elementary treatment of these relationships. We first develop the notion of "ideal” and "real” images, corresponding to, respectively, the raw incoming light and the measured signal. This framework separates the filtering and geometric aspects of imaging. We then consider how to synthesize one view of a surface from another; if the transformation between the two views is affine, it emerges that this is possible if and only if the singular values of the affine matrix are positive. Next, we consider how to combine the information in several views of a surface into a single output image. By developing a new tool called "frequency segmentation,” we show how this can be done despite not knowing the blurring kernel.
INDEX TERMS
Reconstruction, restoration, sharpening and deblurring, smoothing.
CITATION
Justin Domke, Yiannis Aloimonos, "Image Transformations and Blurring", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.31, no. 5, pp. 811-823, May 2009, doi:10.1109/TPAMI.2008.133
REFERENCES
[1] B. Bascle, A. Blake, and A. Zisserman, “Motion Deblurring and Super-Resolution from an Image Sequence,” Proc. Fourth European Conf. Computer Vision, pp. 573-582, 1996.
[2] R.N. Bracewell, Two-Dimensional Imaging. Prentice Hall, 1995.
[3] R.N. Bracewell, K.-Y. Chang, A.K. Jha, and Y.-H. Wang, “Affine Theorem for Two-Dimensional Fourier Transform,” Electronics Letters, vol. 29, no. 3, p. 304, 1993.
[4] S. Farsiu, D. Robinson, M. Elad, and P. Milanfar, “Fast and Robust Multi-Frame Super-Resolution,” IEEE Trans. Image Processing, vol. 13, no. 10, pp. 1327-1344, Oct. 2004.
[5] R.I. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision, second ed. Cambridge Univ. Press, 2004.
[6] D. Hong and V. Kenneth, “Effects of Point-Spread Function on Calibration and Radiometric Accuracy of CCD Camera,” Applied Optics, vol. 43, no. 3, pp. 665-670, 2004.
[7] H. Ji and C. Fermuller, “Wavelet-Based Super-Resolution Reconstruction: Theory and Algorithm,” Proc. Ninth European Conf. Computer Vision, 2006.
[8] T. Lindeberg, “On the Axiomatic Foundations of Linear Scale-Space: Combining Semi-Group Structure with Causality versus Scale Invariance,” technical report, Dept. of Numerical Analysis and Computing Science, Royal Inst. of Tech nology, 1994.
[9] T. Lindeberg and J. Gårding, “Shape-Adapted Smoothing in Estimation of 3-D Shape Cues from Affine Deformations of Local 2-D Brightness Structure,” Image Vision Computing, vol. 15, no. 6, pp. 415-434, 1997.
[10] K. Mikolajczyk and C. Schmid, “Scale & Affine Invariant Interest Point Detectors,” Int'l J. Computer Vision, vol. 60, no. 1, pp. 63-86, 2004.
[11] K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir, and L. Van Gool, “A Comparison of Affine Region Detectors,” Int'l J. Computer Vision, vol. 65, nos. 1-2, pp. 43-72, 2005.
[12] S.C. Park, M.K. Park, and M.G. Kang, “Super-Resolution Image Reconstruction: A Technical Overview,” IEEE Signal Processing Magazine, vol. 20, no. 3, pp. 21-36, 2003.
[13] S. Ravela, “Shaping Receptive Fields for Affine Invariance,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 725-730, 2004.
[14] J. Starck, E. Pantin, and F. Murtagh, Deconvolution in Astronomy: A Review, 2002.
[15] J.V. Stone, “Shape from Texture: Textural Invariance and the Problem of Scale in Perspective Images of Textures Surface,” Proc. British Machine Vision Conf., pp. 181-187, 1990.
[16] J.V. Stone and S.D. Isard, “Adaptive Scale Filtering: A General Method for Obtaining Shape from Texture,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 17, no. 7, pp. 713-718, July 1995.
[17] L. Wang, S.B. Kang, R. Szeliski, and H.-Y. Shum, “Optimal Texture Map Reconstruction from Multiple Views,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, no. 1, pp. 347-354, 2001.
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