Conditional Random Field Model for Robust Multi-Focus Image Fusion

In this paper, a novel multi-focus image fusion algorithm based on conditional random field optimization (mf-CRF) is proposed. It is based on an unary term that includes the combined activity estimation of both high and low frequencies of the input images, while a spatially varying smoothness term i...

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Bibliographic Details
Published in:IEEE transactions on image processing 2019-11, Vol.28 (11), p.5636-5648
Main Authors: Bouzos, Odysseas, Andreadis, Ioannis, Mitianoudis, Nikolaos
Format: Article
Language:English
Subjects:
Algorithms
Computer vision
Conditional random field (CRF)
Conditional random fields
Dictionaries
Energy conservation
energy minimization
Estimation
Frequency estimation
High frequency
Image fusion
Image processing
independent component analysis (ICA)
Infrared imagery
Market shares
Minimization
multi-focus image fusion
Optimization
Smoothness
Spectral methods
Transforms
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Summary:In this paper, a novel multi-focus image fusion algorithm based on conditional random field optimization (mf-CRF) is proposed. It is based on an unary term that includes the combined activity estimation of both high and low frequencies of the input images, while a spatially varying smoothness term is introduced, in order to align the graph-cut solution with boundaries of focused and defocused pixels. The proposed model retains the advantages of both spatial-domain methods and multi-spectral methods and by solving an energy minimization problem and finds an optimal solution for the multi-focus image fusion problem. Experimental results demonstrate the effectiveness of the proposed method that outperforms current state-of-the-art multi-focus image fusion algorithms in both qualitative and quantitative comparisons. In this paper, the successful application of the mf-CRF model in multi-modal image fusion (visible-infrared and medical) is also presented.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2019.2922097