Abstract
In this work, a method which can generate high quality inverse halftone images from halftone images is proposed. This method uses least-mean-square (LMS) trained filters to establish the relationship between the current processing position and its corresponding neighbor positions in each kind of halftone image. This includes direction binary search (DBS), error diffusion, dot diffusion, and ordered dithering. After which, the support region which is used for features extracting can be obtained by relabeling the LMS-trained filters by order of importance. Two features are used in this work: 1) the probability of black pixel occurrence at each position in the support region, and 2) the probability of mean occurrence which is obtained from all pixels in the support region. According to these data, the probabilities of all possible grayscale values appearance at current processing position can be obtained by Bayesian theorem. Consequently, the final output at this position is the grayscale value with highest probability. Experimental results show that the image quality and memory consumption of the proposed method are superior to Mese-Vaidyanathan’s method.
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Ulichney, R.: Digital Halftoning. MIT Press, Cambridge (1987)
Knuth, D.E.: Digital halftones by dot diffusion. ACM Trans. Graph. 6(4) (October 1987)
Mese, M., Vaidyanathan, P.P.: Optimized halftoning using dot diffusion and methods for inverse halftoning. IEEE Trans. on Image Processing 9, 691–709 (2000)
Floyd, R.W., Steinberg, L.: An adaptive algorithm for spatial gray scale. In: Proc. SID 75 Digest. Society for information Display, pp. 36–37 (1975)
Jarvis, J.F., Judice, C.N., Ninke, W.H.: A survey of techniques for the display of continuous-tone pictures on bilevel displays. Comp. Graph. Image Proc. 5, 13–40 (1976)
Stucki, P.: MECCA-A multiple-error correcting computation algorithm for bilevel image hardcopy reproduction. Res. Rep. RZ1060, IBM Res. Lab., Zurich, Switzerland (1981)
Ostromoukhov, V.: A simple and efficient error-diffusion algorithm. In: Computer Graphics (Proceedings of SIGGRAPH 2001), pp. 567–572 (2001)
Shiau, J.N., Fan, Z.: A set of easily implementable coefficients in error diffusion with reduced worm artifacts. In: SPIE, vol. 2658, pp. 222–225 (1996)
Lin, Q., Allebach, J.P.: Color FM screen design using DBS algorithm. In: Proc. SPIE, vol. 3300, pp. 353–361 (1998)
Agar, A.U., Allebach, J.P.: Model-based color halftoning using direct binary search. IEEE Trans. on Image Processing 14, 1945–1959 (2005)
Chang, P.-C., Yu, C.-S.: Neural net classification and LMS reconstruction to halftone images. In: Proc. SPIE, vol. 3309, pp. 592–602 (1998)
Mese, M., Vaidyanathan, P.P.: Look-Up Table (LUT) Method for Inverse Halftoning. IEEE Trans. on Image Processing 10(10), 1566–1578 (2001)
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Liu, YF., Guo, JM., Lee, JD. (2009). Inverse Halftoning Based on Bayesian Theorem. In: Wada, T., Huang, F., Lin, S. (eds) Advances in Image and Video Technology. PSIVT 2009. Lecture Notes in Computer Science, vol 5414. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92957-4_12
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DOI: https://doi.org/10.1007/978-3-540-92957-4_12
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