[1] HOWE J D, MILLER M A, BLUMER R V et al. Polarization sensing for target acquisition and mine detection[C], 202-213(2000).

[2] KYBA C C M, AUBÉ M, BARÁ S et al. Multiple angle observations would benefit visible band remote sensing using night lights[J]. Journal of Geophysical Research： Atmospheres, 127, e2021jd036382(2022).

[3] LIN Y H, HUANG H H, WANG Y J et al. Image-based polarization detection and material recognition[J]. Optics Express, 30, 39234-39243(2022).

[4] ZENG L F, WANG B, XIN G X et al. A fault location method based on polarization analysis for coal mine[J]. Frontiers in Earth Science, 11, 1097314(2023).

[5] YANG Y B, ZHANG G B, YAO C L et al. Application of induced polarization method in mineral resource exploration[J]. Sustainability, 15, 3840(2023).

[6] WANG S F, MENG J, ZHOU Y et al. Polarization image fusion algorithm using NSCT and CNN[J]. Journal of Russian Laser Research, 42, 443-452(2021).

[7] DEB MAJUMDER B, ROY J K, PADHEE S. Recent advances in multifunctional sensing technology on a perspective of multi-sensor system： a review[J]. IEEE Sensors Journal, 19, 1204-1214(2019).

[8] ZHAN L, ZHUANG Y, HUANG L. Infrared and visible images fusion method based on discrete wavelet transform[J]. Journal of Computers, 28, 57-71(2017).

[9] MA J Y, MA Y, LI C. Infrared and visible image fusion methods and applications： a survey[J]. Information Fusion, 45, 153-178(2019).

[10] CANDÈS AND DAVID L DONOHO B E J[M]. Curvelets： A Surprisingly Effective Nonadaptive Representation for Objects with Edges(1999).

[11] DO M N, VETTERLI M. The contourlet transform： an efficient directional multiresolution image representation[J]. IEEE Transactions on Image Processing, 14, 2091-2106(2005).

[12] CUNHA A LDA, ZHOU J P, DO M N. The nonsubsampled contourlet transform： theory， design， and applications[J]. IEEE Transactions on Image Processing, 15, 3089-3101(2006).

[13] ZHANG Q, LIU Y, BLUM R S et al. Sparse representation based multi-sensor image fusion for multi-focus and multi-modality images： a review[J]. Information Fusion, 40, 57-75(2018).

[14] [14] 朱攀， 刘泽阳， 黄战华. 基于DTCWT和稀疏表示的红外偏振与光强图像融合［J］. 光子学报， 2017， 46（12）： 1210002. doi: 10.3788/gzxb20174612.1210002ZHUP， LIUZ Y， HUANGZH H. Infrared polarization and intensity image fusion based on dual-tree complex wavelet transform and sparse representation［J］. Acta Photonica Sinica， 2017， 46（12）： 1210002.（in Chinese）. doi: 10.3788/gzxb20174612.1210002

[15] MA J Y, YU W, LIANG P W et al. FusionGAN： a generative adversarial network for infrared and visible image fusion[J]. Information Fusion, 48, 11-26(2019).

[16] ZHANG J C, SHAO J B, CHEN J L et al. PFNet： an unsupervised deep network for polarization image fusion[J]. Optics Letters, 45, 1507-1510(2020).

[17] LI H, WU X J. DenseFuse： a fusion approach to infrared and visible images[J]. IEEE Transactions on Image Processing, 28, 2614-2623(2019).

[18] PARK J, LEE J Y et al. CBAM： Convolutional Block Attention Module[M]. Lecture Notes in Computer Science, 3-19(2018).

[19] ZHANG J C, SHAO J B, CHEN J L et al. Polarization image fusion with self-learned fusion strategy[J]. Pattern Recognition, 118, 108045(2021).

[20] BÖTTCHER A, WENZEL D. The Frobenius norm and the commutator[J]. Linear Algebra and Its Applications, 429, 1864-1885(2008).

[21] [21] 巩文哲， 褚金奎， 成昊远， 等. 基于无监督学习和注意力机制的水下偏振图像融合［J］. 光学 精密工程， 2023， 31（21）： 3212-3220. doi: 10.37188/OPE.20233121.3212GONGW ZH， CHUJ K， CHENGH Y， et al. Underwater polarization image fusion based on unsupervised learning and attention mechanisms［J］. Opt. Precision Eng.， 2023， 31（21）： 3212-3220.（in Chinese）. doi: 10.37188/OPE.20233121.3212

[22] GLOROT X, BENGIO Y. Understanding the difficulty of training deep feedforward neural networks[C], 249-256(2010).

[23] NENCINI F, GARZELLI A, BARONTI S et al. Remote sensing image fusion using the curvelet transform[J]. Information Fusion, 8, 143-156(2007).

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

[25] LIU Y, CHEN X, WARD R K et al. Image fusion with convolutional sparse representation[J]. IEEE Signal Processing Letters, 23, 1882-1886(2016).

[26] LIU Y, LIU S P, WANG Z F. A general framework for image fusion based on multi-scale transform and sparse representation[J]. Information Fusion, 24, 147-164(2015).

[27] [27] 杨艳春， 李娇， 王阳萍. 图像融合质量评价方法研究综述［J］. 计算机科学与探索， 2018， 12（7）： 1021-1035. doi: 10.3778/j.issn.1673-9418.1710001YANGY CH， LIJ， WANGY P. Review of image fusion quality evaluation methods［J］. Journal of Frontiers of Computer Science and Technology， 2018， 12（7）： 1021-1035.（in Chinese）. doi: 10.3778/j.issn.1673-9418.1710001

[28] 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).

[29] XYDEAS C S, PETROVIĆ V. Objective image fusion performance measure[J]. Electronics Letters, 36, 308(2000).

[30] MA J Y, YU W, CHEN C et al. Pan-GAN： an unsupervised pan-sharpening method for remote sensing image fusion[J]. Information Fusion, 62, 110-120(2020).

[31] WU W T, LIU H, LI L L et al. Application of local fully convolutional neural network combined with YOLOv5 algorithm in small target detection of remote sensing image[J]. PLoS One, 16(2021).

[32] PARK I, KIM S. Performance indicator survey for object detection[C], 13, 284-288(2020).