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Journal articles on the topic 'Halftoning'

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Author: Grafiati

Published: 4 June 2021

Last updated: 2 February 2022

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1

Chen, Guang Xue, Qi Feng Chen, Jing Lei Tai, and Jiang Wen Liu. "Halftoning Method and Reproduction of Color Gamut in Digital Image Output." Applied Mechanics and Materials 421 (September 2013): 138–43. http://dx.doi.org/10.4028/www.scientific.net/amm.421.138.

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Image halftoning is the core technique of digital image output, which determines the quality of the color and tonal gradation reproduction. In this paper, the existing halftoning techniques were analyzed and evaluated, such as FM halftoning and AM halftoning, etc. And a new halftoning mode, frequency conversion amplitude modulation (FCAM), was proposed. This method is still based on AM halftoning, but it uses the different screen frequency for halftoning image of the different color version, so as to avoid moiré fringes on the smaller superposition angle. The use of new halftoning model can achieve halftoning image output which is more than 4 colors (such as 6 colors or 7 colors) without visual moirè pattern when overprinting. By the new technique, high-fidelity printing is achieved, and the color gamut of the output image is expanded, and we can get more vivid realistic effect of image transmission. We tested color gamut expansion of 6 colors and 7 colors high-fidelity printing, and obtained the expected conclusion by quantitative analysis.

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2

Ortega-Sánchez, Noé, Diego Oliva, Erik Cuevas, Marco Pérez-Cisneros, and Angel A. Juan. "An Evolutionary Approach to Improve the Halftoning Process." Mathematics 8, no. 9 (September 22, 2020): 1636. http://dx.doi.org/10.3390/math8091636.

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The techniques of halftoning are widely used in marketing because they reduce the cost of impression and maintain the quality of graphics. Halftoning converts a digital image into a binary image conformed by dots. The output of the halftoning contains less visual information; a possible benefit of this task is the reduction of ink when graphics are printed. The human eye is not able to detect the absence of information, but the printed image stills have good quality. The most used method for halftoning is called Floyd-Steinberger, and it defines a specific matrix for the halftoning conversion. However, most of the proposed techniques in halftoning use predefined kernels that do not permit adaptation to different images. This article introduces the use of the harmony search algorithm (HSA) for halftoning. The HSA is a popular evolutionary algorithm inspired by the musical improvisation. The different operators of the HSA permit an efficient exploration of the search space. The HSA is applied to find the best configuration of the kernel in halftoning; meanwhile, as an objective function, the use of the structural similarity index (SSIM) is proposed. A set of rules are also introduced to reduce the regular patterns that could be created by non-appropriate kernels. The SSIM is used due to the fact that it is a perception model used as a metric that permits comparing images to interpret the differences between them numerically. The aim of combining the HSA with the SSIM for halftoning is to generate an adaptive method that permits estimating the best kernel for each image based on its intrinsic attributes. The graphical quality of the proposed algorithm has been compared with classical halftoning methodologies. Experimental results and comparisons provide evidence regarding the quality of the images obtained by the proposed optimization-based approach. In this context, classical algorithms have a lower graphical quality in comparison with our proposal. The results have been validated by a statistical analysis based on independent experiments over the set of benchmark images by using the mean and standard deviation.

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3

Mao, Ruiyi, Utpal Sarkar, Robert Ulichney, and Jan P. Allebach. "3D Halftoning." Electronic Imaging 2017, no. 18 (January 29, 2017): 147–55. http://dx.doi.org/10.2352/issn.2470-1173.2017.18.color-048.

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4

Schmaltz, Christian, Pascal Gwosdek, Andrés Bruhn, and Joachim Weickert. "Electrostatic Halftoning." Computer Graphics Forum 29, no. 8 (November 10, 2010): 2313–27. http://dx.doi.org/10.1111/j.1467-8659.2010.01716.x.

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5

Miceli, Christopher M. "Inverse halftoning." Journal of Electronic Imaging 1, no. 2 (April 1, 1992): 143. http://dx.doi.org/10.1117/12.57675.

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6

Allebach, J., and T. Pappas. "Digital halftoning." IEEE Signal Processing Magazine 20, no. 4 (July 2003): 12–13. http://dx.doi.org/10.1109/msp.2003.1215227.

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7

Zhaohui Sun. "Video halftoning." IEEE Transactions on Image Processing 15, no. 3 (March 2006): 678–86. http://dx.doi.org/10.1109/tip.2005.863023.

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8

G Freeman, K. "Digital halftoning." Displays 9, no. 3 (July 1988): 149. http://dx.doi.org/10.1016/0141-9382(88)90011-x.

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9

Shapiro, Linda. "Digital halftoning." Computer Vision, Graphics, and Image Processing 41, no. 3 (March 1988): 382. http://dx.doi.org/10.1016/0734-189x(88)90109-0.

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10

Li, Mei, Erhu Zhang, Yutong Wang, Jinghong Duan, and Cuining Jing. "Inverse Halftoning Methods Based on Deep Learning and Their Evaluation Metrics: A Review." Applied Sciences 10, no. 4 (February 23, 2020): 1521. http://dx.doi.org/10.3390/app10041521.

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Inverse halftoning is an ill-posed problem that refers to the problem of restoring continuous-tone images from their halftone versions. Although much progress has been achieved over the last decades, the restored images still suffer from detail loss and visual artifacts. Recent studies show that inverse halftoning methods based on deep learning are superior to other traditional methods, and thus this paper aimed to systematically review the inverse halftone methods based on deep learning, so as to provide a reference for the development of inverse halftoning. In this paper, we firstly proposed a classification method for inverse halftoning methods on the basis of the source of halftone images. Then, two types of inverse halftoning methods for digital halftone images and scanned halftone images were investigated in terms of network architecture, loss functions, and training strategies. Furthermore, we studied existing image quality evaluation including subjective and objective evaluation by experiments. The evaluation results demonstrated that methods based on multiple subnetworks and methods based on multi-stage strategies are superior to other methods. In addition, the perceptual loss and the gradient loss are helpful for improving the quality of restored images. Finally, we gave the future research directions by analyzing the shortcomings of existing inverse halftoning methods.

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11

Mese, M., and P. P. Vaidyanathan. "Recent advances in digital halftoning and inverse halftoning methods." IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications 49, no. 6 (June 2002): 790–805. http://dx.doi.org/10.1109/tcsi.2002.1010034.

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12

Li, Hua, and David Mould. "Contrast-aware Halftoning." Computer Graphics Forum 29, no. 2 (May 2010): 273–80. http://dx.doi.org/10.1111/j.1467-8659.2009.01596.x.

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13

Kautz, Jan, and Hans-Peter Seidel. "Real-Time Halftoning." Journal of Graphics Tools 7, no. 4 (January 2002): 27–31. http://dx.doi.org/10.1080/10867651.2002.10487569.

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14

Baqai, F. A., J. H. Lee, A. U. Agar, and J. P. Allebach. "Digital color halftoning." IEEE Signal Processing Magazine 22, no. 1 (January 2005): 87–96. http://dx.doi.org/10.1109/msp.2005.1407718.

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15

Streit, L., and J. Buchanan. "Importance Driven Halftoning." Computer Graphics Forum 17, no. 3 (August 1998): 207–17. http://dx.doi.org/10.1111/1467-8659.00268.

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16

Pang, Wai-Man, Yingge Qu, Tien-Tsin Wong, Daniel Cohen-Or, and Pheng-Ann Heng. "Structure-aware halftoning." ACM Transactions on Graphics 27, no. 3 (August 2008): 1–8. http://dx.doi.org/10.1145/1360612.1360688.

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17

Hausner, Alejo. "Versatile Decorative Halftoning." Journal of Graphics Tools 13, no. 2 (January 2008): 1–12. http://dx.doi.org/10.1080/2151237x.2008.10129257.

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18

Gwosdek, Pascal, Christian Schmaltz, Joachim Weickert, and Tanja Teuber. "Fast electrostatic halftoning." Journal of Real-Time Image Processing 9, no. 2 (December 24, 2011): 379–92. http://dx.doi.org/10.1007/s11554-011-0236-3.

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19

Mese, M., and P. P. Vaidyanathan. "Optimized halftoning using dot diffusion and methods for inverse halftoning." IEEE Transactions on Image Processing 9, no. 4 (April 2000): 691–709. http://dx.doi.org/10.1109/83.841944.

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20

HUANG, W., A. SU, and Y. KUO. "Neural network based method for image halftoning and inverse halftoning." Expert Systems with Applications 34, no. 4 (May 2008): 2491–501. http://dx.doi.org/10.1016/j.eswa.2007.04.013.

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21

Mese, M., and P. P. Vaidyanathan. "Tree-structured method for LUT inverse halftoning and for image halftoning." IEEE Transactions on Image Processing 11, no. 6 (June 2002): 644–55. http://dx.doi.org/10.1109/tip.2002.1014996.

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22

Zhou, Xiao, Rui Zhi Shi, Da Li, Jing An, and Chao Sheng. "An Image Anti-Forgery Algorithm Based on Conceal Zone Special Halftoning." Applied Mechanics and Materials 731 (January 2015): 147–52. http://dx.doi.org/10.4028/www.scientific.net/amm.731.147.

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Based on analysis of current situation, this paper develops a new anti-forgery algorithm based on conceal zone special halftoning, which realizes image anti-forgery in printing process. It brings forward the algorithm thought, and then sets up two halftoning models of template and threshold matrix. By numbering and extracting the manuscript pixels, it realizes the exact dividing of conceal zone and common zone through scan line filling algorithm. Then the method of generating halftone image by using the two halftoning models is studied. An experiment is performed to study the interior dot structure of the halftone image and its character. The experiment testifies that this algorithm can implant special shape dots different from traditional dot shapes in special zones of one image or multiple images, in which the anti-forgery is realized by observing the special zone’s dots.

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23

Kwon, Young-Man, Kyuho Kim, and Hyeong-Joon Kwon. "Digital Halftoning using DCT." Journal of the Institute of Webcasting, Internet and Telecommunication 13, no. 6 (December 31, 2013): 79–85. http://dx.doi.org/10.7236/jiibc.2013.13.6.79.

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24

Lau, D. L., G. R. Arce, and N. C. Gallagher. "Green-noise digital halftoning." Proceedings of the IEEE 86, no. 12 (1998): 2424–44. http://dx.doi.org/10.1109/5.735449.

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25

Kacker, D., and J. P. Allebach. "Joint halftoning and watermarking." IEEE Transactions on Signal Processing 51, no. 4 (April 2003): 1054–68. http://dx.doi.org/10.1109/tsp.2003.809369.

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26

Zixiang Xiong, M. T. Orchard, and K. Ramchandran. "Inverse halftoning using wavelets." IEEE Transactions on Image Processing 8, no. 10 (1999): 1479–83. http://dx.doi.org/10.1109/83.791977.

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27

Pappas, T. N., J. P. Allebach, and D. L. Neuhoff. "Model-based digital halftoning." IEEE Signal Processing Magazine 20, no. 4 (July 2003): 14–27. http://dx.doi.org/10.1109/msp.2003.1215228.

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28

Inoue, Kohei, Kenji Hara, and Kiichi Urahama. "Integral Image-Based Halftoning." Journal of The Institute of Image Information and Television Engineers 67, no. 8 (2013): J303—J305. http://dx.doi.org/10.3169/itej.67.j303.

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29

Son, C. H., and H. M. Park. "Sparsity-based inverse halftoning." Electronics Letters 48, no. 14 (2012): 832. http://dx.doi.org/10.1049/el.2012.0670.

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30

Adler, R. L., B. P. Kitchens, M. Martens, C. P. Tresser, and C. W. Wu. "The mathematics of halftoning." IBM Journal of Research and Development 47, no. 1 (January 2003): 5–15. http://dx.doi.org/10.1147/rd.471.0005.

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31

Gotsman, Craig. "Halftoning of image sequences." Visual Computer 9, no. 5 (May 1993): 255–66. http://dx.doi.org/10.1007/bf01908448.

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32

Wu, Huisi, Tien-Tsin Wong, and Pheng-Ann Heng. "Parallel structure-aware halftoning." Multimedia Tools and Applications 67, no. 3 (March 8, 2012): 529–47. http://dx.doi.org/10.1007/s11042-012-1048-6.

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33

Gooran, Sasan. "A Novel Hybrid Amplitude Modulated/Frequency Modulated Halftoning Based on Multilevel Halftoning." Journal of Imaging Science and Technology 50, no. 2 (2006): 157. http://dx.doi.org/10.2352/j.imagingsci.technol.(2006)50:2(157).

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34

Ashour, Amira S., Yanhui Guo, Ahmed Refaat Hawas, and Chunlai Du. "Optimised halftoning and inverse halftoning of dermoscopic images for supporting teledermoscopy system." IET Image Processing 13, no. 3 (February 28, 2019): 529–36. http://dx.doi.org/10.1049/iet-ipr.2018.6166.

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35

Jeon, Gwang Gil. "A New Error-Diffusion Dithering Kernel for Image Halftoning." Advanced Materials Research 717 (July 2013): 506–10. http://dx.doi.org/10.4028/www.scientific.net/amr.717.506.

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Signal image halftoning approach quantizes each pixel to one bit binary pixel. During the process of error diffusion, the quantization difference at each pixel is fed back to the input to diffuse the quantization difference among the adjacent color or gray-level pixels. This paper proposes a new kernel for error diffusion to enhance halftoning quality by minimizing average error after the process. Simulation results section confirms that the obtained color and green channel result images are satisfactory.

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36

Jin, Yang, Hong Guo Wang, Xue Qin Liang, and Wei Tian Zhang. "Study on Theoretical Color Model under Multilevel Halftoning." Applied Mechanics and Materials 731 (January 2015): 3–6. http://dx.doi.org/10.4028/www.scientific.net/amm.731.3.

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Along with the developing of color imaging technique, the application of digital multilevel imaging device increases, such as digital printing press, color printer, electronic engraver for gravure printing and so on. Based on multilevel halftoning technique, some important items, i.e. count of color element, area percentage of color elements, relationship between the percentages of color element and multilevel dots are studied and summarized. Finally, based on Neugebauer equation, a theoretical color model under the condition of multilevel halftoning is constructed.

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37

Li, Donghui, Takuma Kiyotomo, Takahiko Horiuchi, Midori Tanaka, and Kaku Shigeta. "Texture-aware Error Diffusion Algorithm for Multi-level Digital Halftoning." Journal of Imaging Science and Technology 64, no. 5 (September 1, 2020): 50410–1. http://dx.doi.org/10.2352/j.imagingsci.technol.2020.64.5.050410.

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Abstract Digital halftoning is a technique for converting a continuous-tone image into a quantized image to reproduce it on a digital printing device. Error diffusion (ED) is an algorithm that has proven to be effective for the halftoning process, and it has been widely applied to digital printing tasks. However, in images reproduced using conventional ED algorithms based on the signal processing theory, the texture of objects is often lost. In this study, we propose a texture-aware ED algorithm for multi-level digital halftoning. First, we generate multiple mapped images with different brightness levels through nonlinear transformation. For each mapped image, we adopt a texture-aware binary error diffusion method to obtain multiple halftone images. Finally, we generate a multi-level halftone image from the multiple halftone images. We test the algorithm on an actual printer, compare the results with those of the current raster image processor software and classical ED algorithms, and observe that our algorithm outputs better results.

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38

Zhang, Erhu, Yan Zhang, and Jinghong Duan. "Color Inverse Halftoning Method with the Correlation of Multi-Color Components Based on Extreme Learning Machine." Applied Sciences 9, no. 5 (February 27, 2019): 841. http://dx.doi.org/10.3390/app9050841.

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Look-up table (LUT) based method is a popular and effective way for inverse halftoning. However, it still has very large development space to improve the reconstructed color image quality for color halftone images, because most of the existing color inverse halftoning methods are the simple extension of LUT methods to each color components separately. To this end, this paper presents a novel color inverse halftoning method by exploiting the correlation of multi-color components. Through considering all existent contone values with the same halftone pattern in three color component tables, we firstly propose a concept of common pattern. Then the extreme learning machine (ELM) is employed to estimate the contone values for nonexistent patterns according to common patterns in color LUT, which can not only improve the fitting precision of nonexistent values but also has fast transformation speed. Experimental results show that the proposed method achieves a better image quality when compared to previously published methods.

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39

Zou, Dan, Wen Hua Qian, Jin Xu, Zheng Peng Zhao, and Zhi Ming Chen. "Non-Photorealistic Rendering Effect of Halftoning." Applied Mechanics and Materials 373-375 (August 2013): 473–77. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.473.

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To provide with an effective method for non-photorealistic rendering for computer generated images with halftoning artistic appearances from 2D images motivates our work in this paper. The methods proposed in this paper are inspired by improved error diffusion method and image enhancement, and the whole diffusion algorithm is based on the average threshold. Firstly, source image should be transferred to the gray image. Then, error diffusion, spread component and parameter confirming can be used to obtain more details. Experimental results show that the proposed method can simulate the halftoning effect in real time, and blemishes of the results can be eliminated.

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40

Wang, Dong Xing, and Su Chen. "Improved Subdivision Based Halftoning Algorithm." Advanced Materials Research 267 (June 2011): 368–71. http://dx.doi.org/10.4028/www.scientific.net/amr.267.368.

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The subdivision based halftoning algorithm works in a statistical way. Analysis of its output shows that, error accumulation and rounding operation may produce big quantization error, and there may be artifacts in its output. Some improvements have been proposed. First, a procedure is added to find cases with big quantization error, and to solve the problem. Second, instead of propagating error sequentially as in the algorithm, propagating error along random directions has been tested to reduce the artifacts in the output. Third, propagating error along a direction, in which the quantization error of each subpart is the minimum, has also been tested to improve the output quality. Halftoning tests show that the improved algorithm produces clearer output than the original.

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41

Duan, Hua Wei, and Guang Xue Chen. "An Improved Design of Stochastic Halftoning without Error Diffusion for Raster Image Processor." Advanced Materials Research 317-319 (August 2011): 2427–31. http://dx.doi.org/10.4028/www.scientific.net/amr.317-319.2427.

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The performance of modern printing machine depends heavily upon the raster image processor (RIP) whereof it is equipped with. The conventional digital halftoning technology used in RIP is characterized as error diffusion, which is suitable for imaging machines with low resolutions, often resulting in correlated artifacts and transient edges. We report an innovative stochastic halftoning method without error diffusion for RIP by using an optimized random number generator (RNG). The RIP system we proposed has simple computation and stochastic features of ink spots. This research is supportive of enhancing raster image quality oriented to printing machines with high physical resolutions.

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42

Tharayil, Jose J., E. S. Karthik Kumar, and Neena Susan Alex. "Visual Cryptography Using Hybrid Halftoning." Procedia Engineering 38 (2012): 2117–23. http://dx.doi.org/10.1016/j.proeng.2012.06.254.

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43

Schmaltz, C., P. Gwosdek, and J. Weickert. "Multi-Class Anisotropic Electrostatic Halftoning." Computer Graphics Forum 31, no. 6 (May 10, 2012): 1924–35. http://dx.doi.org/10.1111/j.1467-8659.2012.03072.x.

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44

Wong, Lai-Yan, and Yuk-Hee Chan. "Feature-preserving multilevel halftoning algorithm." Journal of Electronic Imaging 21, no. 4 (November 27, 2012): 043016. http://dx.doi.org/10.1117/1.jei.21.4.043016.

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45

Cohen, Simon J. "Halftoning with random correlated noise." Journal of Electronic Imaging 1, no. 2 (April 1, 1992): 209. http://dx.doi.org/10.1117/12.57524.

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46

Scheermesser, Thomas. "Iterative halftoning using spectral constraints." Journal of Electronic Imaging 4, no. 2 (April 1, 1995): 179. http://dx.doi.org/10.1117/12.203078.

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47

Veryovka, O., and J. Buchanan. "Comprehensive Halftoning of 3D Scenes." Computer Graphics Forum 18, no. 3 (September 1999): 13–22. http://dx.doi.org/10.1111/1467-8659.00323.

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48

Broja, Manfred, and Olof Bryngdahl. "Quantization noise in electronic halftoning." Journal of the Optical Society of America A 10, no. 4 (April 1, 1993): 554. http://dx.doi.org/10.1364/josaa.10.000554.

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49

Stevenson, R. L. "Inverse halftoning via MAP estimation." IEEE Transactions on Image Processing 6, no. 4 (April 1997): 574–83. http://dx.doi.org/10.1109/83.563322.

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50

Li-Ming Chen and Hsueh-Ming Hang. "An adaptive inverse halftoning algorithm." IEEE Transactions on Image Processing 6, no. 8 (August 1997): 1202–9. http://dx.doi.org/10.1109/83.605420.

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