Abstract
Image fusion is a technique to obtain a new more informative image from various similar or dissimilar sources and sensors toward generating an enhanced status and identity of the observed object or scene. Multi-focus image fusion plays an important role on the improvement of the perceptual quality, especially within spatial and temporal textures. In this chapter, several focus measures for multi-focus image fusion were reviewed. These measures consist of variance, energy of image gradient (EOG), Tenenbaum’s method, and sum-modified-Laplacian (SML), which can be easily implemented because of its definition in the spatial domain. An efficient scheme to assess focus measures according to the capability of distinguishing focused image blocks from defocused image blocks is proposed. Experiments and numerical results demonstrated that sum-modified-Laplacian can achieve better performance than other focus measures.
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Acknowledgements
The authors would like to thank the anonymous reviewers for their valuable suggestions. This work is jointly supported by National Natural Science Foundation of China (60375008), EXPO Technologies Special Project of National Key Technologies R&D Program (2004BA908B07), Shanghai World EXPO Technologies Special Project (04DZ05807), China Ph.D. Discipline Special Foundation (20020248029), China Aviation Science Foundation (02D57003), Aerospace Supporting Technology Foundation (2003-1.3 0 2).
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Jing, Z., Pan, H., Li, Y., Dong, P. (2018). Evaluation of Focus Measures in Multi-Focus Image Fusion. In: Non-Cooperative Target Tracking, Fusion and Control. Information Fusion and Data Science. Springer, Cham. https://doi.org/10.1007/978-3-319-90716-1_15
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DOI: https://doi.org/10.1007/978-3-319-90716-1_15
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