A VQ-Based blind super-resolution algorithm

Authors:
Jianping Qiao;Ju Liu;Guoxia Sun
Affiliations:
School of Information Science and Engineering, Shandong University, Jinan, Shandong, China;School of Information Science and Engineering, Shandong University, Jinan, Shandong, China;School of Information Science and Engineering, Shandong University, Jinan, Shandong, China
Venue:
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Year:
2005

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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 l1 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.