Perceptual fusion of infrared and visible images through a hybrid multi-scale decomposition with Gaussian and bilateral filters

By Zhiqiang Zhou, Sun Li, Mingjie Dong. Information Fusion, 2016

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FusionDN: A Unified Densely Connected Network for Image Fusion (2020, methodology): “These comparative methods contain traditional, deep learning-based and general fusion methods, such as HMSD (Zhou et al. 2016), GTF (Ma et al. 2016), FPDE (Bavirisetti, Xiao, and Liu 2017), DenseFuse (Li and Wu 2018) and FusionGAN (Ma et al. 2019) for infrared and visible fusion, GFF (Li, Kang, and…”
U2Fusion: A Unified Unsupervised Image Fusion Network (2020, methodology): “We compare U2Fusion with ﬁve state-of-the-art methods: HMSD [42], GTF [19], DenseFuse [17], FusionGAN [5], and DDcGAN [24].”
DDcGAN: A Dual-Discriminator Conditional Generative Adversarial Network for Multi-Resolution Image Fusion (2020, methodology): “To verify the effectiveness of our proposed DDcGAN, we compare it with seven state-of-the-art image fusion methods, including directional discrete cosine transform and principal component analysis (DDCTPCA) [14], hybrid multi-scale decomposition (HMSD) [47], fourth-order partial differential equatio”