Abstract
In this paper, a new inverse half toning method has been proposed for reconstructing low resolution line halftone images. This reconstruction is done in order to authenticate an image in question. The reconstructed image is compared with its original image in terms of standard image quality metrics such as peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM). The existing inverse halftone methods have rarely considered line halftone images which are normally of low resolution and the quality of the inverse halftone largely depends on the characteristics like frequency or shape of halftone dots. Our proposed inverse halftone technique consists of two parts: at first, the resolution (in lines per inch, lpi) of an input image is estimated and a low level image from the binary line halftone image is constructed. In the second phase, gray level continuous image is generated from the low level description and the lpi information. The method is based on learning based pattern classification techniques namely, neural nets. A comparative study shows that the proposed method outperforms many existing inverse halftone techniques while dealing with line halftone images.
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Lau, D.L., Arce, G.R.: Modern Digital Halftoning. Marcel Dekker, New York (2001)
Knuth, D.E.: Digital halftones by dot diffusion. Published ACM Trans. Graph. (TOG) 6(4), 245–273 (1987)
Analoui, M., Allebach, J.P.: Model based halftoning using direct binary search In: Proceedings SPIE Human Vision, Visual Digital Display III, vol. 1666, pp. 96–108, San Jose, CA (1992)
Kite, T.D., Damera-Venkata, N., Evans, B.L., Bovik, A.C.: A fast, high-quality inverse halftoning algorithm for error diffused halftones. IEEE Trans. Image Process. 9(9), 1583–1592 (2000)
Chung, K.L., Wu, S.T.: Inverse halftoning algorithm using edge-based lookup table approach. IEEE Trans. Image Process. 14(10), 1583–1589 (2005)
Reserve Bank of India, High Level RBI Group Suggests Steps to Check Menace of Fake Notes, Press release: 2009–2010/232 (2009)
Procedure Manuals, prepared by Directorate of Forensic Science, Ministry of Home Affairs, Government of India. http://www.dfs.gov.in
Counterfeit Banknotes, report of the parliamentary office of science and technology, UK (1996). www.parliament.uk/briefing-papers/POST-PN-77.pdf
Huang, W.B., Su, A.W.Y., Kuo, Y.H.: Neural network based method for image halftoning and inverse halftoning. Expert Syst. Appl. 34(4), 2491–2501 (2008)
Chang, P.C., Yu, C.S.: Neural net classification and LMS reconstruction to halftone images. In: Proceedings SPIE Visual Communications and Image Processing, vol. 3309, pp. 592–602 (1998)
Hore, A., Ziou, D.: Image quality metrics: PSNR vs. SSIM. In: Proceedings IEEE International Conference of Pattern Recognition, pp. 2366–2369 (2010)
Foi, A., Katkovnik, V., Egiazarian, K., Astola, J.: Inverse halftoning based on the anisotropic LPA-ICI deconvolution. In: Proceedings of the International TICSP Workshop Spectral Method Multirate Signal Processing (SMMSP), pp. 49-56, Vienna (2004)
Neelamani, R., Nowak, R., Baraniuk, R.: WInHD: wavelet-based inverse halftoning via deconvolution. IEEE Trans. Image Proc. 6(12), 1673–1687 (2002)
Stevenson, R.: Inverse halftoning via MAP estimation. IEEE Trans. Image Proc. 6, 574–583 (1997)
Mese, M., Vaidyanathan, P.P.: Look-up table (LUT) method for inverse halftoning. IEEE Trans. Image Proc. 10(10), 1566–1578 (2001)
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Halder, B., Garain, U., Darbar, R., Mondal, A.C. (2015). Inverse of Low Resolution Line Halftone Images for Document Inspection. In: Garain, U., Shafait, F. (eds) Computational Forensics. IWCF IWCF 2012 2014. Lecture Notes in Computer Science(), vol 8915. Springer, Cham. https://doi.org/10.1007/978-3-319-20125-2_9
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DOI: https://doi.org/10.1007/978-3-319-20125-2_9
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