Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/12429
Title: Kurtosis-based super-resolution algorithm
Authors: Qiao, J
Liu, J
Meng, X
Siu, WC 
Keywords: Kurtosis
Optimization
Super-resolution
Issue Date: 2009
Source: Proceedings - 2009 IEEE International Conference on Multimedia and Expo, ICME 2009, 2009, 5202439, p. 73-76 How to cite?
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.
Description: 2009 IEEE International Conference on Multimedia and Expo, ICME 2009, New York, NY, 28 June-3 July 2009
URI: http://hdl.handle.net/10397/12429
ISBN: 9781424442911
DOI: 10.1109/ICME.2009.5202439
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