Sparse representation-based demosaicing method for microgrid polarimeter imagery

Junchao Zhang, Haibo Luo, Rongguang Liang, Ashfaq Ahmed, Xiangyue Zhang, Bin Hui, Zheng Chang

Research output: Contribution to journalArticle

10 Scopus citations

Abstract

To address the key image interpolation issue in microgrid polarimeters, we propose a machine learning model based on sparse representation. The sparsity and non-local self-similarity priors are used as regularization terms to enhance the stability of an interpolation model. Moreover, to make the best of the correlation among different polarization orientations, patches of different polarization channels are joined to learn adaptive sub-dictionary. Synthetic and real images are used to evaluate the interpolated performance. The experimental results demonstrate that our proposed method achieves state-of-the-art results in terms of quantitative measures and visual quality.

Original languageEnglish (US)
Pages (from-to)3265-3268
Number of pages4
JournalOptics letters
Volume43
Issue number14
DOIs
StatePublished - Jul 15 2018

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics

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  • Cite this

    Zhang, J., Luo, H., Liang, R., Ahmed, A., Zhang, X., Hui, B., & Chang, Z. (2018). Sparse representation-based demosaicing method for microgrid polarimeter imagery. Optics letters, 43(14), 3265-3268. https://doi.org/10.1364/OL.43.003265