Multi-Focus Image Fusion via PAPCNN and Fractal Dimension in NSST Domain

Abstract

Multi-focus image fusion is a popular technique for generating a full-focus image, where all objects in the scene are clear. In order to achieve a clearer and fully focused fusion effect, in this paper, the multi-focus image fusion method based on the parameter-adaptive pulse-coupled neural network and fractal dimension in the nonsubsampled shearlet transform domain was developed. The parameter-adaptive pulse coupled neural network-based fusion rule was used to merge the low-frequency sub-bands, and the fractal dimension-based fusion rule via the multi-scale morphological gradient was used to merge the high-frequency sub-bands. The inverse nonsubsampled shearlet transform was used to reconstruct the fused coefficients, and the final fused multi-focus image was generated. We conducted comprehensive evaluations of our algorithm using the public Lytro dataset. The proposed method was compared with state-of-the-art fusion algorithms, including traditional and deep-learning-based approaches. The quantitative and qualitative evaluations demonstrated that our method outperformed other fusion algorithms, as evidenced by the metrics data such as QAB/F, QE, QFMI, QG, QNCIE, QP, QMI, QNMI, QY, QAG, QPSNR, and QMSE. These results highlight the clear advantages of our proposed technique in multi-focus image fusion, providing a significant contribution to the field.