Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1544
Title: An efficient fractal-based algorithm for image magnification
Authors: Lai, Cheung-ming
Lam, Kin-man Kenneth
Chan, Yuk-hee
Siu, Wan-chi
Subjects: Error compensation
Fractals
Image coding
Image enhancement
Image reconstruction
Image resolution
Iterative methods
Issue Date: 2004
Publisher: IEEE
Source: ISIMP 2004 : proceedings of 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing : October 20-22, 2004, Hong Kong, p. 571-574.
Abstract: An efficient image magnification algorithm based on the Iterated Function System (IFS) is proposed in this paper. This IFS-based image magnification method employs the self-similarity property instead of the conventional interpolation approach. This self-similarity makes it possible to generate images of higher resolution. Our proposed algorithm can maintain not only the high frequency information that preserves the edges, but also effectively remove blocky artifacts from a magnified image. To further improve the quality of the high-resolution images, the error image or residual errors are considered. In addition, our algorithm can combine with other magnification algorithms. Experimental results show that our proposed algorithm can increase the PSNR by 2.60dB, 2.30dB and 0.83dB on average when compared to the traditional fractal-based image magnification algorithm, the bicubic spline interpolation method and the recently proposed fractal-based image magnification algorithm (IEUF), respectively. If residual errors are considered, the PSNR of our algorithm can be further increased by about 0.22dB. When combining our algorithm with IEUF, the PSNR can further improve by 0.4dB, on average.
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Type: Conference Paper
URI: http://hdl.handle.net/10397/1544
ISBN: 0-7803-8687-6
Appears in Collections:EIE Conference Papers & Presentations

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