A SVM-based blur identification algorithm for image restoration and resolution enhancement

Authors:
Jianping Qiao;Ju Liu
Affiliations:
School of Information Science and Engineering, Shandong University, Jinan, Shandong, China;School of Information Science and Engineering, Shandong University, Jinan, Shandong, China
Venue:
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Year:
2006

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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.