Abstract
The problem of see-through cancelation in digital images of double-sided documents is addressed. We show that a nonlinear convolutional data model proposed elsewhere for moderate show-through can also be effective on strong back-to-front interferences, provided that the recto and verso pure patterns are estimated jointly. To this end, we propose a restoration algorithm that does not need any classification of the pixels. The see-through PSFs are estimated off-line, and an iterative procedure is then employed for a joint estimation of the pure patterns. This simple and fast algorithm can be used on both grayscale and color images and has proved to be very effective in real-world cases. The experimental results we report in this paper demonstrate that our algorithm outperforms the ones based on linear models with no need to tune free parameters and remains computationally inexpensive despite the nonlinear model and the iterative solution adopted. Strategies to overcome some of the residual difficulties are also envisaged.
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References
Almeida L.B.: Separating a real-life nonlinear image mixture. J. Mach. Learn. Res. 6, 1199–1230 (2005)
Almeida, M.S.C., Almeida, L.B.: Separating nonlinear image mixtures using a physical model trained with ICA. IEEE Intl. Workshop on Machine Learning for Signal Processing, Maynooth, Ireland (2006)
Almeida M.S.C., Almeida L.B.: Wavelet-based separation of nonlinear show-through and bleed-through image mixtures. Neurocomputing 72, 57–70 (2008)
Andrews H.C., Hunt B.R.: Digital Image Restoration. Prentice Hall, Englewood Cliffs, NJ (1977)
Deville Y., Hosseini S.: Recurrent networks for separating extractable-target nonlinear mixtures. Part I: Non-blind configurations. Signal Process. 89, 378–393 (2009)
Honkela, A.: Advances in variational Bayesian nonlinear blind source separation. PhD dissertation, Helsinki University of Technology, Report D10 (2005)
Hyvärinen A., Pajunen P.: Nonlinear independent component analysis. Neural Networks 12, 429–439 (1999)
Hyvärinen A., Karhunen J., Oja E.: Independent Component Analysis. Wiley, New York, NY (2001)
Luenberger D.G.: Linear and Nonlinear Programming. Addison-Wesley, Reading, MA (1984)
Martinelli F., Salerno E., Gerace I., Tonazzini A.: Non-linear model and constrained ML for removing back-to-front interferences from recto-verso documents. Pattern Recogn. 45, 596–605 (2012)
Merrikh-Bayat F., Babaie-Zadeh M., Jutten C.: Linear-quadratic blind source separating structure for removing show-through in scanned documents. IJDAR 14, 319–333 (2011)
Merrikh-Bayat, F., Babaie-Zadeh, M., Jutten, C.: Using non-negative matrix factorization for removing show-through. Proc. LVA-ICA, Saint Malo, France (2010)
Moghaddam R.F., Cheriet M.: Low-quality document image modeling and enhancement. IJDAR 11, 183–201 (2009)
Ophir, B., Malah, D.: Show-through cancellation in scanned images using blind source separation techniques. Proc. IEEE-ICIP 2007 III, pp. 233–236 (2007)
Sharma G.: Show-through cancellation in scans of duplex printed documents. IEEE Trans. Image Process. 10, 736–754 (2001)
Tonazzini A., Bedini L., Salerno E.: Independent component analysis for document restoration. IJDAR 7, 17–27 (2004)
Tonazzini A., Salerno E., Mochi M., Bedini L.: Bleed-through removal from degraded documents using a color decorrelation method. Lecture Notes Comput. Sci. 3163, 229–240 (2004)
Tonazzini A., Salerno E., Bedini L.: Fast correction of bleed-through distortion in grayscale documents by a blind source separation technique. IJDAR 10, 17–25 (2007)
Tonazzini, A., Bianco, G., Salerno, E.: Registration and enhancement of double-sided degraded manuscripts acquired in multispectral modality. Proc. IEEE-ICDAR 2009, pp. 546–550 (2009)
Tonazzini A., Gerace I., Martinelli F.: Multichannel blind separation and deconvolution of images for document analysis. IEEE Trans. Image Process. 19, 912–925 (2010)
Wang, Q., Tan, C.L.: Matching of double-sided document images to remove interference. In: Proceedings of IEEE-CVPR 2001, vol. 1, pp. 1084–1089 (2001)
Wang, Q., Xia, T., Li, L., Tan, C.L.: Document image enhancement using directional wavelet. In: Proceedings of IEEE-CVPR 2003, vol. 2, pp. 534–539 (2003)
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Salerno, E., Martinelli, F. & Tonazzini, A. Nonlinear model identification and see-through cancelation from recto–verso data. IJDAR 16, 177–187 (2013). https://doi.org/10.1007/s10032-012-0183-y
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DOI: https://doi.org/10.1007/s10032-012-0183-y