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Improving defocus blur measures using robust regularization
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DOI：10.1016/j.icte.2021.11.001.png)
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Abstract
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This paper presents a robust regularization scheme for the enhancement of initial blur map in single image blur detection. The proposed technique considers the structural differences between guidance and initial blur map. Specifically, we propose to improve the initial blur map by optimizing a nonconvex energy function that jointly leverages structural information from guidance and blur map. Nonconvex problem is solved by majorize-minimize algorithm and an improved blur map is obtained that is adequately smooth and has better edge-preserving properties. Ultimately, this results in high-quality blur segmentation. (C) 2021 The Author(s). Published by Elsevier B.V. on behalf of The Korean Institute of Communications and Information Sciences.
Keywords:
Blur measure
Blur segmentation
Robust regularization
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