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An image fusion framework using morphology and sparse representation

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Abstract

Image fusion is the process which aims to integrate the relevant and complementary information from a set of images into a single comprehensive image. Sparse representation (SR) is a powerful technique used in a wide variety of applications like denoising, compression and fusion. Building a compact and informative dictionary is the principal challenge in these applications. Hence, we propose a supervised classification based learning technique for the fusion algorithm. As an initial step, each patch of the training data set is pre-classified based on their gradient dominant direction. Then, a dictionary is learned using K-SVD algorithm. With this universal dictionary, sparse coefficients are estimated using greedy OMP algorithm to represent the given set of source images in the dominant direction. Finally, the Euclidean norm is used as a distance measure to reconstruct the fused image. Experimental results on different types of source images demonstrate the effectiveness of the proposed algorithm with conventional methods in terms of visual and quantitative evaluations.

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Acknowledgment

We express our sincere thanks to Yu Liu [33] for sharing MST-SR toolbox. Multi-focus data set and Multi-modal CT/MR image pairs used in our experiments are available at [1, 2]. The artificially blurred multi-focus image pairs used in the proposed method are generated by convolving each ground truth image (available at [3]) with a 7 × 7 average filter centered at the left part and right part respectively.

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Authors and Affiliations

  1. Department of ECE, R.M.D Engineering College, Anna University, Chennai, India

    N. Aishwarya & C. Bennila Thangammal

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  1. N. Aishwarya
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  2. C. Bennila Thangammal
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Correspondence to N. Aishwarya.

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Aishwarya, N., Bennila Thangammal, C. An image fusion framework using morphology and sparse representation. Multimed Tools Appl 77, 9719–9736 (2018). https://doi.org/10.1007/s11042-017-5562-4

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  • DOI: https://doi.org/10.1007/s11042-017-5562-4

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