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Basis pursuit denoising-based image superresolution using a redundant set of atoms

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Abstract

Digital investigations are very difficult to conduct from low-quality images generated by low-quality sensors. Therefore, we present a novel superresolution (SR) scheme that applies SR and denoising simultaneously, using the concept of sparse representation. For SR, a low-resolution (LR) input image is scaled up using our recently described adaptive interpolation scheme, and for each patch of the LR input, a vector of the sparse coefficients is then sought using a basis pursuit denoising sparse-coding algorithm instead of orthogonal matching pursuit. A high-resolution output is generated from the given LR input using the recovered vector of the sparse coefficients over a redundant set of atoms, i.e., an overcomplete dictionary. For the proposed technique, we modified the sparse-coding method of the K-SVD dictionary training approach by incorporating an efficient \(l_{1}\)-regularized least-squares method, i.e., a feature-sign search algorithm. Experimental evaluations validate the effectiveness of the proposed SR scheme.

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Notes

  1. The software package available at http://www.cs.technion.ac.il/~ronrubin/software.html.

  2. The software package is available at http://ai.stanford.edu/~hllee/softwares/nips06-sparsecoding.htm.

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Acknowledgments

This research is supported by (1) the MSIP (Ministry of Science, ICT & Future Planning), Korea in the ICT R&D Program 2014 (R0112-14-1014), and (2) The Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2013R1A1A2012904).

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Correspondence to Sung Wook Baik.

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Sajjad, M., Mehmood, I., Abbas, N. et al. Basis pursuit denoising-based image superresolution using a redundant set of atoms. SIViP 10, 181–188 (2016). https://doi.org/10.1007/s11760-014-0724-6

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  • DOI: https://doi.org/10.1007/s11760-014-0724-6

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