Abstract
In this paper, a novel method of blind Super-Resolution (SR) image restoration is presented. First, a learning based blur identification method is proposed to identify the blur parameter in which Sobel operator and Vector Quantization (VQ) are used for extracting feature vectors. Then a super-resolution image is reconstructed by a new hybrid MAP/POCS method where the data fidelity term is minimized by l 1 norm and regularization term is defined on the high frequency sub-bands offered by Stationary Wavelet Transform (SWT) to incorporate the smoothness of the discontinuity field. Simulation results demonstrate the effectiveness and robustness of our method.
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© 2005 Springer-Verlag Berlin Heidelberg
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Qiao, J., Liu, J., Sun, G. (2005). A VQ-Based Blind Super-Resolution Algorithm. In: Huang, DS., Zhang, XP., Huang, GB. (eds) Advances in Intelligent Computing. ICIC 2005. Lecture Notes in Computer Science, vol 3644. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11538059_34
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DOI: https://doi.org/10.1007/11538059_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28226-6
Online ISBN: 978-3-540-31902-3
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