[1] Liu Y, Chen X, Peng H et al. Multi-focus image fusion with a deep convolutional neural network[J]. Information Fusion, 36, 191-207(2017).

[2] Chen Q J, Li Y, Chai Y Z. A multi-focus image fusion algorithm based on depth learning[J]. Laser & Optoelectronics Progress, 55, 071015(2018).

[3] Zhan K, Li Q Q, Teng J C et al. Multifocus image fusion using phase congruency[J]. Journal of Electronic Imaging, 24, 033014(2015).

[4] Liu Y, Wang Z F. Simultaneous image fusion and denoising with adaptive sparse representation[J]. IET Image Processing, 9, 347-357(2015).

[5] Burt P, Adelson E. The Laplacian pyramid as a compact image code[J]. IEEE Transactions on Communications, 31, 532-540(1983).

[6] Li H, Manjunath B S, Mitra S K. Multisensor image fusion using the wavelet transform[J]. Graphical Models and Image Processing, 57, 235-245(1995).

[7] Zhang Q, Guo B L. Multifocus image fusion using the nonsubsampled contourlet transform[J]. Signal Processing, 89, 1334-1346(2009).

[8] Li J, Yang Y C, Dang J W et al. Multi-focus image fusion based on NSCT and guided filtering[J]. Laser & Optoelectronics Progress, 55, 071007(2018).

[9] Li S T, Kang X D, Hu J W. Image fusion with guided filtering[J]. IEEE Transactions on Image Processing, 22, 2864-2875(2013).

[10] Wang K, Li Z W, Zhu C D et al. Local stereo matching algorithm based on secondary guided filtering[J]. Laser & Optoelectronics Progress, 56, 116-122(2019).

[11] Li S T, Kang X D, Hu J W et al. Image matting for fusion of multi-focus images in dynamic scenes[J]. Information Fusion, 14, 147-162(2013).

[12] Liu Y, Liu S P, Wang Z F. Multi-focus image fusion with dense SIFT[J]. Information Fusion, 23, 139-155(2015).

[13] Li H F, Chai Y, Yin H P et al. Multifocus image fusion and denoising scheme based on homogeneity similarity[J]. Optics Communications, 285, 91-100(2012).

[14] Mustafa H T, Yang J, Zareapoor M. Multi-scale convolutional neural network for multi-focus image fusion[J]. Image and Vision Computing, 85, 26-35(2019).

[15] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 770-778(2016).

[16] Huang G. Liu Z,van der Maaten L, et al. Densely connected convolutional networks. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 2261-2269(2017).

[17] Lin M, Chen Q. -03-04)[2019-07-21]. https:∥arxiv.xilesou., top/abs/1312, 4400(2014).

[19] Lewis J J. O'Callaghan R J, Nikolov S G, et al. Pixel- and region-based image fusion with complex wavelets[J]. Information Fusion, 8, 119-130(2007).

[20] Yin M, Liu X N, Liu Y et al. Medical image fusion with parameter-adaptive pulse coupled neural network in nonsubsampled shearlet transform domain[J]. IEEE Transactions on Instrumentation and Measurement, 68, 49-64(2019).

[21] Nejati M, Samavi S, Shirani S. Multi-focus image fusion using dictionary-based sparse representation[J]. Information Fusion, 25, 72-84(2015).

[22] Wang Z, Bovik A C, Sheikh H R et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 13, 600-612(2004).

[23] Kumar M, Dass S. A total variation-based algorithm for pixel-level image fusion[J]. IEEE Transactions on Image Processing, 18, 2137-2143(2009).

[24] Wang Z, Bovik A C. A universal image quality index[J]. IEEE Signal Processing Letters, 9, 81-84(2002).