REFERENCES
- Lau DL, Arce GR. Modern Digital Halftoning, 2001, 2nd edition (Marcel Dekker, New York).
- Mese M, Vaidyanathan PP. Recent advances in digital halftoning and inverse halftoning methods. IEEE Trans. Circuits Syst I: Fundam. Theory Appl., 2002, 49, 790–805.
- Ulichney R. Digital Halftoning, illustrated edition, 1987 (MIT Press, Cambridge, MA).
- Asano T. Digital halftoning: algorithm engineering challenges. IEICE Trans. Inf. Syst., 2003, E86-D, 159–178.
- Kim SH, Allebach JP. Impact of HVS on model based-based halftoning. IEEE Trans. Image Process., 2002, 11, 258–269.
- Judice CN, Jarvis JF, Ninke WH. Using ordered dither to display continuous tone pictures on an AC plasma panel. Proc. SID, 1974, 15, 161–169.
- Jarvis JF, Judice CN, Ninke WH. A survey of techniques for the display of continuous-tone pictures on bilevel displays. Comput. Graph. Image Process., 1976, 5, 13–40.
- Zhang Y, Webber RE. Space diffusion: an improved parallel halftoning technique using space filling curves. Proc. SIGGRAPH, 1993, 93, 305–312.
- Saito H, Kobayashi N. Evolutionary computation approaches to halftoning algorithm, Proc. IEEE Conf. on Evolutionary computation: ICEC’94, Orlando, FL, USA, June 1994, IEEE, pp. 787–791.
- Bayer BE. An optimum method for two level rendition of continuous-tone pictures, Proc. IEEE Int. Conf. on Communication: ICC’73, Seattle, WA, USA, June 1973, IEEE Computer Society, Conference Record, (26-11)–(26-15).
- Ulichney R. The void-and-cluster method for dither array generation. Proc. SPIE, 1993, 1913, 332–343.
- Zhang Y. Space-filling curve ordered dither. Comput. Graph., 1998, 22, 559–563.
- Guo JM. High efficiency ordered dither block truncation coding with dither array LUT and its scalable coding application. Digit. Signal Process., 2010, 20, 97–110.
- Zhang Y. Adaptive ordered dither. Graph. Models Image Process., 1997, 59, 49–53.
- Ostromoukhov V, Hersch RD, Amidror I. Rotated dispersed dither: a new technique for digital halftoning. Proc. SIGGRAPH, 1994, 94, 123–130.
- Wang PW. Entropy-constrained halftoning using multipath tree coding. IEEE Trans. Image Process., 1997, 6, 1567–1579.
- Anastassiou D. Error diffusion coding for A/D conversion. IEEE Trans. Circuits Syst., 1989, 36, 1175–1186.
- Anastassiou D. Neural net based digital halftoniong of images, Proc. IEEE Int. Symp. on Circuits and systems: ISCAS’88, Espoo, Finland,June 1988, Helsinki University of Technology, Vol. 1, pp. 507–510.
- Shoop BL, Ressler EK. Optimal error diffusion for digital halftoning using an optical neural network, Proc. 1st IEEE Int. Conf. on Image processing: ICIP’94, Austin, TX, USA,November 1994, IEEE, pp. 1036–1040.
- Aomori H, Otake T, Takahashi N, Tanaka M. Sigma-delta cellular neural network for 2D modulation. Neural Networks, 2008, 21, 349–357.
- Huang WB, Alvin WYS, Kuo YH. Neural network based method for image halftoning and inverse halftoning. Expert Syst. Appl., 2008, 34, 2491–2501.
- Abe Y. A new method for designing a dither matrix. IEICE Trans. Fundam. Electron. Commun. Comput. Sci., 2002, E85-A, 1702–1709.
- Lieberman DJ, Allebach JP. A dual interpretation for direct binary search and its applications for tone reproduction and texture quality. IEEE Trans. Image Process., 2000, 9, 1950–1963.
- Yu S.-N, Lin C.-N. An efficient paradigm for wavelet-based image processing using: cellular neural networks. Int. J. Circuit Theory Appl., 2010, 38, 527–542.
- Geqay R, Liu T. Nonlinear modelling and prediction with feedforward and recurrent networks. Physica D, 1997, 108, 119–134.
- Seker S, Ayaz E, Urkcan ET. Elman’s recurrent neural network applications to condition monitoring in nuclear power plant and rotating machinery. Eng. Appl. Artif. Intell., 2003, 16, 647–656.
- Kollias S, Anastassiou D. A preogressive scheme for digital image halftoning, coding of halftones, and reconstruction. IEEE J. Sel. Area Commun., 1992, 10, 944–951.
- Elman J. Finding structure in time. Cogn. Sci., 1990, 14, 179–211.
- Jordan MI. ‘Serial order: a parallel distributed processing approach’, Report 8604, Institute for Cognitive Science Report, UC San Diego, San Diego, CA, USA, 1986.
- Lee C, Allebach JP. The hybrid screen – improving the breed. IEEE Trans. Image Process., 2010, 19, 435–450.
- Pang W.-H, Qu Y, Wong T.-T, Cohen-Or D, Heng P.-A. Structure-aware halftoning. ACM Trans. Graph., 2008, 27, 89:1–89:8.
- Haykin S. Neural Networks: A Comprehensive Foundation, 2001, 2nd edition (Pearson Education Asia, Hong Kong).
- Näsänen R. Visibility of halftone dot textures. IEEE Trans. Syst. Man Cybern., 1984, 14, 920–924.
- Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process., 2004, 13, 600–612.
- Jurs PC, Bakken GA, McClelland HE. Computational methods for the analysis of chemical sensor array data from volatile analytes. Chem. Rev., 2000, 100, 2649–2678.
- Hierlemann A, Gutierrez-Osuna R. Higher-order chemical sensing. Chem. Rev., 2008, 108, 563–613.
- Wang Z, Bovik AC. Mean squared error: love it or leave it? IEEE Signal Process. Mag., 2009, 26, 98–99.
- Wang Z, Bovik AC. A universal quality index. IEEE Signal Process. Lett., 2002, 9, 81–84.
- Evans BL, Monga V, Venkata DN. Halftoning Toolbox for MATLAB, Version 1.1 [online], 2002. Available at: <http://www.ece.utexas.edu/∼bevans/projects/halftoning/> Accessed 15 July, 2009.