Abstract
An image magnification method with a Gradient Vector Flow (GVF) constraint-based anisotropic diffusion model is proposed in this letter. A Low-Resolution (LR) image is first magnified using bilinear interpolation, and then an iterative image restoration method, with the use of an anisotropic diffusion model and a Gaussian moving-average constraint, is applied to the magnified image. The estimated GVF of a High-Resolution (HR) image can be used to remove the jagged effect and to preserve the textural structure in the image. Meanwhile, the use of the Gaussian moving-average LR model can provide a data fidelity constraint, which renders a magnified image closer to the ideal HR version. Experimental results show that the proposed method can improve the quality of magnified images in terms of both objective and subjective criteria.
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Supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (No. PolyU 5199/06E), and by the National Natural Science Foundation of China (No.60472036, No.60431020, No. 60402036, No.60772069), and the Natural Science Foundation of Beijing (No.4062006).
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Li, X., Lam, KM. & Shen, L. An image magnification algorithm using the GVF constraint model. J. Electron.(China) 25, 568–571 (2008). https://doi.org/10.1007/s11767-008-0002-2
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DOI: https://doi.org/10.1007/s11767-008-0002-2