Abstract
Blur identification is usually necessary in image restoration. In this paper, a novel blur identification algorithm based on Support Vector Machines (SVM) is proposed. In this method, blur identification is considered as a multi-classification problem. First, Sobel operator and local variance are used to extract feature vectors that contain information about the Point Spread Functions (PSF). Then SVM is used to classify these feature vectors. The acquired mapping between the vectors and corresponding blur parameter provides the identification of the blur. Meanwhile, extension of this method to blind super-resolution image restoration is achieved. After blur identification, a super-resolution image is reconstructed from several low-resolution images obtained by different foci. Simulation results demonstrate the feasibility and validity of the method.
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© 2006 Springer-Verlag Berlin Heidelberg
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Qiao, J., Liu, J. (2006). A SVM-Based Blur Identification Algorithm for Image Restoration and Resolution Enhancement. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4252. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893004_4
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DOI: https://doi.org/10.1007/11893004_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46537-9
Online ISBN: 978-3-540-46539-3
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