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access icon free Adaptive regularised l 2-boosting on clustered sparse coefficients for single image super-resolution

© The Institution of Engineering and Technology
Received 12/08/2016, Accepted 25/01/2017, Revised 29/11/2016, Published 26/01/2017

In this study, the authors propose a novel approach for single image super-resolution. Their method is based on the idea of learning a mapping function, which can reveal the intrinsic relationship between sparse coefficients of low-resolution (LR) and high-resolution (HR) image patch pairs with respect to their individual dictionaries. Adaptive regularised l 2-boosting algorithm is proposed to learn this type of mapping function. Specifically, to reduce time consumption, the authors cluster training patches into several clusters. Within each cluster, a pair of dictionaries for LR and HR image patches is jointly trained. Adaptive regularised l 2-boosting algorithm is then employed to obtain the function. Thus, in a reconstruction stage, for each given input LR image patch, the authors can effectively estimate its corresponding HR image patch. Their extensive experimental results demonstrated that the proposed method achieves a performance of similar quality performance to that of the top methods.

Inspec keywords: pattern clustering; image reconstruction; learning (artificial intelligence); image resolution

Other keywords: HR image patch; adaptive regularised l2-boosting algorithm; single image super-resolution; high-resolution image patch; image reconstruction; mapping function learning; low-resolution image patch; clustered sparse coefficients; LR image patch

Subjects: Computer vision and image processing techniques; Knowledge engineering techniques; Optical, image and video signal processing

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