Kurtosis-based super-resolution algorithm

Authors:
Jianping Qiao;Ju Liu;Xiangzeng Meng;Wan-Chi Siu
Affiliations:
School of Communication, Shandong Normal University and School of Information Science and Engineering, Shandong University, Jinan, China and Hong Kong Polytechnic University, Hung Hom, Hong Kong;School of Information Science and Engineering, Shandong University, Jinan, China;School of Communication, Shandong Normal University, Jinan, China;Hong Kong Polytechnic University, Hung Hom, Hong Kong
Venue:
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Year:
2009

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Abstract

A kurtosis-based super-resolution image reconstruction algorithm is proposed in this paper. Firstly, we give the definition of the kurtosis image and analyze its two properties: (i) the kurtosis image is Gaussian noise invariant, and (ii) the absolute value of a kurtosis image becomes smaller as the the image gets smoother. Then we build a constrained absolute local kurtosis maximization function to estimate the high-resolution image by fusing multiple blurred low-resolution images corrupted by intensive white Gaussian noise. The Lagrange multiplier is used to solve the combinatorial optimization problem. Experimental results demonstrate that the proposed method is better than the conventional algorithms in terms of visual inspection and robustness, using both synthetic and real world examples under severe noise background. It has an improvement of 0.5 to 2.0 dB in PSNR over other approaches.