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Defocus Hyperspectral Image Deblurring with Adaptive Reference Image and Scale Map

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Abstract

Defocus blur is one of the primary problems among hyperspectral imaging systems equipped with simple lenses. Most of the previous deblurring methods focus on how to utilize structure information of a single channel, while ignoring the characteristics of hyperspectral images. In this work, we analyze the correlations and differences among spectral channels, and propose a deblurring framework for defocus hyperspectral images. First, we divide the hyperspectral image channels into two sets, and the set with less blur is treated as a group of spectral bases. Then, according to the inherent correlations of spectral channels, a reference image can be derived from the spectral bases to guide the restoration of blurry channels. Finally, considering the disagreement between the reference image and the ground truth, a scale map based on gradient similarity is introduced as a prior in the deblurring framework. The experimental results on public dataset demonstrate that the proposed method outperforms several image deblurring methods in both visual effect and quality metrics.

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Acknowledgements

We thank all anonymous reviewers for their valuable comments.

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Authors and Affiliations

  1. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China

    De-Wang Li, Lin-Jing Lai & Hua Huang

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  1. De-Wang Li
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  2. Lin-Jing Lai
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  3. Hua Huang
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Correspondence to Hua Huang.

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Li, DW., Lai, LJ. & Huang, H. Defocus Hyperspectral Image Deblurring with Adaptive Reference Image and Scale Map. J. Comput. Sci. Technol. 34, 569–580 (2019). https://doi.org/10.1007/s11390-019-1927-7

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  • DOI: https://doi.org/10.1007/s11390-019-1927-7

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