Abstract
A single frame image super-resolution reconstruction technique is proposed with two stages contains tetrolet regularization and tetrolet learning. In the first stage, the tetrolet regularization is used to estimate an initial high-resolution image. In the second stage, the tetrolet coefficients at finer scales of the estimated high-resolution image are learned locally from a set of high-resolution training images. Finally the fusion of tetrolet reconstruction produces the super-resolution image. Experimental results demonstrated that the proposed method outperforms state-of-the-art super-resolution methods in terms of PSNR index and visual quality.
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Xiao, L., Li, H., Wang, H., Wang, L. (2013). Tetrolet Regularization and Learning for Single Frame Image Super-Resolution. In: Yang, J., Fang, F., Sun, C. (eds) Intelligent Science and Intelligent Data Engineering. IScIDE 2012. Lecture Notes in Computer Science, vol 7751. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36669-7_47
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DOI: https://doi.org/10.1007/978-3-642-36669-7_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36668-0
Online ISBN: 978-3-642-36669-7
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