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Vol. 157, Issue 10, October 2013, pp. 289-294




Super-resolution Based on Two Dictionary-Pairs
Xin Li, Xiuchang Zhu

Jiangsu Province Key Lab on Image Processing & Image Communication, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China
Tel.: +86-2583492416
E-mail: lixin@njupt.edu.cn, zhuxc@njupt.edu.cn


Received: 11 August 2013   /Accepted: 25 September 2013   /Published: 31 October 2013

Digital Sensors and Sensor Sysstems


Abstract: For learning-based super-resolution reconstruction, the selection and training of dictionary play an important role in improving image reconstruction quality. A super-resolution algorithm based on two dictionary-pairs is proposed in this paper. This algorithm selects image's high- and mid-frequency components as the features of high- and low-resolution patches respectively, and gets the first dictionary-pair, i.e. joint-basic high/low-resolution dictionary-pair, by means of joint training. Then it calculates the difference between original high/mid-frequency components and reconstructed high/mid-frequency components with the first dictionary-pair, and composes the second dictionary-pair, i.e. residual high- and low-resolution dictionary-pair. During super-resolution reconstruction, the high-frequency component and residual high-frequency component of low-resolution image are reconstructed respectively with the above two dictionary-pairs. The experiment results show that, the subjective and objective reconstruction quality could be effectively improved by our proposed algorithm.


Keywords: Super-resolution, Learning-based, Dictionary, Sparse representation, Neighbor embedding.


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