Abstract
Document images may exhibit some blurred areas due to a wide number of reasons ranging from digitalization, filtering or even storage problems. Most de-blurring algorithms are hard to implement, slow, and often try to be general, attempting to remove the blur in any kind of image. In the case of text document images, the transition between characters and the paper background has a high contrast. With that in mind, a new algorithm is proposed for de-blurring of textual documents; there is no need to estimate the PSF and the filter proposed can be directed applied to the image. The presented algorithm reached an improvement rate of 17.08% in the SSIM metric.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ukida, H., Konishi, K.: 3D Shape Reconstruction Using Three Light Sources in Image Scanner. IEICE Trans. on Inf. & Syst. E84-D(12), 1713–1721 (2001)
Demoment, G.: Image reconstruction and restoration: Overview of common estimation structures and problems. IEEE Transactions on Acoustics, Speech, & Signal Processing 37(12), 2024–2036 (1989)
Neelamani, R., Choi, H., Baraniuk, R.G.: Wavelet-based deconvolution for ill-conditioned systems. In: Proc. of IEEE ICASSP, vol. 6, pp. 3241–3244 (1999)
Chambolle, A., Lions, P.L.: Image recovery via total variation minimization and related problems. Numerische Mathematik 76(2), 167–188 (1997)
Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D 60, 259–268 (1992)
Roth, S., Black, M.J.: Fields of experts: a framework for learning image priors. CVPR 2, 860–867 (2005)
Joshi, N.S.: Enhancing photographs using content-specific image priors. Phd thesis, University of California, San Diego (2008)
Lins, R.D., Silva, G.F.P., Banergee, S., Kuchibhotla, A., Thielo, M.: Automatically Detecting and Classifying Noises in Document Images. In: ACM-SAC 2010, vol. 1, pp. 33–39. ACM Press (March 2010)
Lins, R.D.: A Taxonomy for Noise in Images of Paper Documents - The Physical Noises. In: Kamel, M., Campilho, A. (eds.) ICIAR 2009. LNCS, vol. 5627, pp. 844–854. Springer, Heidelberg (2009)
Lins, R.D., Oliveira, D.M., Torreão, G., Fan, J., Thielo, M.: Correcting Book Binding Distortion in Scanned Documents. In: Campilho, A., Kamel, M. (eds.) ICIAR 2010, Part II. LNCS, vol. 6112, pp. 355–365. Springer, Heidelberg (2010)
Chang, M.M., Tekalp, A.M., Erdem, A.T.: Blur identification using the bispectrum. IEEE Trans. Signal Process. 39(10), 2323–2325 (1991)
Mayntz, C., Aach, T., Kunz, D.: Blur identification using a spectral inertia tensor and spectral zeros. In: Proc. of IEEE ICIP (1999)
Cannon, M.: Blind deconvolution of spatially invariant image blurs with phase. IEEE Trans. Acoust. Speech Signal Process. 24(1), 56–63 (1976)
Biemond, J., Lagendijk, R.L., Mersereau, R.M.: Iterative methods for image de-blurring. Proc. of the IEEE, 856–883 (1990)
Rekleities, I.M.: Optical flow recognition from the power spectrum of a single blurred image. In: Proc. of IEEE ICIP (1996)
Moghaddam, M.E., Jamzad, M.: Motion blur identification in noisy images using fuzzy sets. In: Proc. IEEE ISSPIT, Athens (2005)
Lokhande, R., Arya, K.V., Gupta, P.: Identification of parameters and restoration of motion blurred images. In: ACM-SAC 2006, Dijon (2006)
Jain, A.K.: Fundamentals of digital image processing. Prentice-Hall, Inc., Upper Saddle River (1989)
Wang, Z., Bovik, A.C., Sheikh, H.R.: Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing 13(4), 600–612 (2004)
ImageJ. GaussianBlur (ImageJ API), http://rsbweb.nih.gov/ij/developer/api/ij/plugin/filter/GaussianBlur.html.
Thouin, P.D., Chang, C.I.: A method for restoration of low-resolution document images. In: IJDAR (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Oliveira, D.M., Lins, R.D., Silva, G.P., Fan, J., Thielo, M. (2013). De-blurring Textual Document Images. In: Kwon, YB., Ogier, JM. (eds) Graphics Recognition. New Trends and Challenges. GREC 2011. Lecture Notes in Computer Science, vol 7423. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36824-0_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-36824-0_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36823-3
Online ISBN: 978-3-642-36824-0
eBook Packages: Computer ScienceComputer Science (R0)