Multi-exposure image fusion based on sparsity blur feature

Yue Que; Weiguo Wan; Hyo Jong Lee

doi:10.1117/12.2326997

29 October 2018 Multi-exposure image fusion based on sparsity blur feature

Yue Que, Weiguo Wan, Hyo Jong Lee

Proceedings Volume 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence; 1083604 (2018) https://doi.org/10.1117/12.2326997
Event: 2018 International Conference on Image, Video Processing and Artificial Intelligence, 2018, Shanghai, China

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.

Citation Download Citation

Yue Que, Weiguo Wan, and Hyo Jong Lee "Multi-exposure image fusion based on sparsity blur feature", Proc. SPIE 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence, 1083604 (29 October 2018); https://doi.org/10.1117/12.2326997

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KEYWORDS

Image fusion

Associative arrays

Chemical species

Image filtering

High dynamic range imaging

Quality measurement

Visualization

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