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An image fusion framework using novel dictionary based sparse representation

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Abstract

Image fusion is an important technique which aims to produce a synthetic result by leveraging the cross information available in the existing data. Sparse Representation (SR) is a powerful signal processing theory used in wide variety of applications like image denoising, compression and fusion. Construction of a proper dictionary with reduced computational efficiency is a major challenge in these applications. Owing to the above criterion, we propose a supervised dictionary learning approach for the fusion algorithm. Initially, gradient information is obtained for each patch of the training data set. Then, the edge strength and information content are measured for the gradient patches. A selection rule is finally employed to select the patches with better focus features for training the over complete dictionary. By the above process, the number of input patches for dictionary training is reduced to a greater extent. At the fusion step, the globally learned dictionary is used to represent the given set of source image patches. Experimental results with various source image pairs demonstrate that the proposed fusion framework gives better visual quality and competes with the existing methodologies quantitatively.

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Acknowledgement

We express our sincere thanks to Rubinstein for providing K-SVD matlab toolbox [4]. We also thank Liu [3] for sharing MST-SR and ASR Toolbox. The source images used in our experiments are available in [1–3].

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Authors and Affiliations

  1. R. M. D Enginnering College, Anna University, Chennai, India

    N. Aishwarya & C. Bennila Thangammal

Authors
  1. N. Aishwarya
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  2. C. Bennila Thangammal
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Corresponding author

Correspondence to N. Aishwarya.

Appendix-1 (Sparse coefficients fusion)

Appendix-1 (Sparse coefficients fusion)

In image fusion, the sparse coefficients capture the local features of source images by optimum selection of dictionary atoms. To select the best sparse coefficients \( {\alpha}_F^t \) at the t th location, the following steps are carried out.

  1. Step 1:

    Activity level measurement- The absolute value of sparse coefficients \( {\alpha}_A^t\;\mathrm{and}\kern0.24em {\alpha}_B^t \) at each coefficient location is computed.

  2. Step 2:

    Fusion of Coefficients- The sum of absolute value of these coefficients \( {\alpha}_A^t\;\mathrm{and}\kern0.24em {\alpha}_B^t \) (L1-norm) are then estimated. Finally, fused sparse coefficients \( {\alpha}_F^t \) are found by selecting either \( {\alpha}_A^t \) or \( {\alpha}_B^t \) with the largest value.

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Aishwarya, N., Bennila Thangammal, C. An image fusion framework using novel dictionary based sparse representation. Multimed Tools Appl 76, 21869–21888 (2017). https://doi.org/10.1007/s11042-017-4583-3

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  • DOI: https://doi.org/10.1007/s11042-017-4583-3

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