Abstract
Image magnification is among the basic image processing operations. The most commonly used techniques for image magnification are based on interpolation method. However, the magnified images produced by the techniques, such as nearest neighbor, bilinear and cubic method, often appear a variety of undesirable image artifacts such as ’blocking’ and ’blurring’ into the several processing for image magnification. In this paper, we propose image magnification method by properties of human visual system which reduce information during transforming from receptors to ganglion cells in retina and magnify information at visual cortex. Our method uses the whole image to exactly detect the edge information of the image and then emphasizes edge information. Experiment results show that the proposed method solves the drawbacks of the image magnification, such as blocking and blurring, and has a higher PSNR and Correlation than the traditional methods.
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References
Battiato, S., Mancuso, M.: An Introduction to the Digital Still Camera Technology. ST Journal of System Research, Special Issue on Image Processing for Digital Still Camera (2001)
Battiato, S., Gallo, G., Stanco, F.: A Locally Adaptive Zooming Algorithm for Digital Images. Image and Vision Computing 20, 805–812 (2002)
Aoyama, K., Ishii, R.: Image Magnification by Using Spectrum Extrapolation. In: IEEE Proceedings of the IECON, vol. 3, pp. 2266–2271 (1993)
Candocia, F.M., Principe, J.C.: Superresolution of Images Based on Local Correlations. IEEE Transactions on Neural Networks 10, 372–380 (1999)
Biancardi, A., Cinque, L., Lombardi, L.: Improvements to Image Magnification. Pattern Recognition 35, 677–687 (2002)
Suyung, L.: A Study on Artificial Vision and Hearing Based on Brain Information Processing. BSRC Research Report (2001)
Gonzalez, R.C., Richard, E.W.: Digital Image Processing, 2nd edn. Prentice-Hall, Englewood Cliffs (2001)
Keys, R.G.: Cubic Convolution Interpolation for Digital Image Processing. IEEE Transaction on Acoustics, Speech, and Signal Processing 29, 1153–1160 (1981)
Salisbury, M., Anderson, C., Lischinski, D., Salesin, D.H.: Scale-dependent Reproduction of Pen-and Ink Illustration. In: Proceedings of SIFFRAPH 96, pp. 461–468 (1996)
Li, X., Orchard, M.T.: New Edge-directed Interpolation. IEEE Transactions on Image Processing, 1521–1527 (2001)
Muresan, D.D., Parks, T.W.: Adaptively quadratic image interpolation. IEEE Transaction on Image Processing, 690–698 (2004)
Johan, H., Nishita, T.: A Progressive Refinement Approach for Image Magnification. In: Proceedings of the 12th Pacific Conference on Computer Graphics and Applications, pp. 351–360 (2004)
Bruce, G.E.: Sensation and Perception, 6th edn. (2002)
Duncan, J.: Selective Attention and the Organization of Visual Information. Journal of Experimental Psychology: General 113, 501–517 (1984)
The HIPR Image Library, http://homepages.inf.ed.ac.uk/rbf/HIPR2/
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Je, SK., Kim, KB., Lee, JY., Cho, JH. (2007). Image Magnification Based on the Properties of Human Visual Processing. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72395-0_113
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DOI: https://doi.org/10.1007/978-3-540-72395-0_113
Publisher Name: Springer, Berlin, Heidelberg
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