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Proceedings Paper

Multi-exposure image fusion based on sparsity blur feature
Author(s): Yue Que; Weiguo Wan; Hyo Jong Lee
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Paper Abstract

Multi-exposure image fusion technique is an important approach to obtain a composite image for High Dynamic Range. The key point of multi-exposure image fusion is to develop an effective feature measurement to evaluate the exposure degree of source images. This paper proposes a novel image fusion method for multi-exposure images with sparsity blur feature. In our algorithm, via the sparse representation and image decomposition, the sparsity blur descriptor is used to measure the exposure level of source image patches to obtain an initial decision map, and then the decision map is refined with gradient domain guided filtering. Experimental results demonstrate that the proposed method can be competitive with or even outperform the state-of-the-art fusion methods in terms of both subjective visual perception and objective evaluation metrics.

Paper Details

Date Published: 29 October 2018
PDF: 5 pages
Proc. SPIE 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence, 1083604 (29 October 2018); doi: 10.1117/12.2326997
Show Author Affiliations
Yue Que, Chonbuk National Univ. (Korea, Republic of)
Weiguo Wan, Chonbuk National Univ. (Korea, Republic of)
Hyo Jong Lee, Chonbuk National Univ. (Korea, Republic of)


Published in SPIE Proceedings Vol. 10836:
2018 International Conference on Image and Video Processing, and Artificial Intelligence
Ruidan Su, Editor(s)

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