Abstract
Based on the compressive sensing theory (CS), various compressive imaging (CI) systems have been developed. Meanwhile, image fusion methods that directly perform on the measurements from multiple CI sensors are also investigated in literatures. In this paper, we presented a multi-focus image fusion method in compressive sensing domain. The main contribution is to introduce a novel clarity level of the random CI measurements without prior geometric information. The CI measurements are sparsely represented with DCT bases which are also projected into the CS domain. Then the sparse coefficients responding to DCT bases are used to guide the fusion of CI measurements of CI sensors. Finally, the fused images are obtained with CS recovery algorithm based on the block compressive sensing (BCS) theory. The simulation results validate the proposed method.
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Cheng, F., Yang, B., Huang, Z. (2014). Compressive Sensing Multi-focus Image Fusion. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45643-9_12
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DOI: https://doi.org/10.1007/978-3-662-45643-9_12
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