An Effective Infrared and Visible Image Fusion Approach via Rolling Guidance Filtering and Gradient Saliency Map

Li, Liangliang; Lv, Ming; Jia, Zhenhong; Jin, Qingxin; Liu, Minqin; Chen, Liangfu; Ma, Hongbing

doi:10.3390/rs15102486

Cited by 9 publications

(1 citation statement)

References 59 publications

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“…These wavelet-based algorithms and improved techniques for image fusion can typically be divided into three key steps, which are outlined below: image decomposition, coefficient fusion, and image reconstruction. Edge-preserving filtering algorithms have become an integral part of image fusion due to their high efficiency [11]. Their fusion structure shares similarities with wavelet-based methods, involving image decomposition into basic-and detail-layers, coefficient fusion, and eventual reconstruction.…”

Section: Introductionmentioning

confidence: 99%

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

Lv,

Jia,

et al. 2023

Mathematics

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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.

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Section: Introductionmentioning

confidence: 99%