An improved hybrid multiscale fusion algorithm based on NSST for infrared–visible images

Peng, Hu; Wang, Chenjun; Li, Dequan; Zhao, Xin

doi:10.1007/s00371-023-02844-8

Cited by 3 publications

(1 citation statement)

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“…A multi-decomposition latent low-rank representation is used to decompose the source image into low-rank and significant parts, and different strategies are used to process the weight map, and finally the fused image is reconstructed. Hu et al [18] proposed an improved hybrid multiscale fusion algorithm. The image is first decomposed into low-frequency and high-frequency parts using the support value transform, and then the prominent edges are further extracted from these support value images using the shearlet transform of NSST.…”

Section: Related Work 21 Multiscale Transform For Image Fusionmentioning

confidence: 99%

A dual-weighted polarization image fusion method based on quality assessment and attention mechanisms

Duan,

Zhang,

Liu

et al. 2023

Front. Phys.

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This paper proposes a dual-weighted polarization image fusion method based on quality assessment and attention mechanisms to fuse the intensity image (S0) and the degree of linear polarization (DoLP). S0 has high contrast and clear details, and DoLP has an outstanding ability to characterize polarization properties, so the fusion can achieve an effective complementation of superior information. We decompose S0 and DoLP into base layers and detail layers. In the base layers, we build a quality assessment unit combining information entropy, no-reference image quality assessment, and local energy to ensure the fused image has high contrast and clear and natural visual perception; in the detail layer, we first extract depth features using the pre-trained VGG19, then construct an attention enhancement unit combining space and channels, and finally effectively improve the preservation of detail information and edge contours in the fused image. The proposed method is able to perceive and retain polarization image features sufficiently to obtain desirable fusion results. Comparing nine typical fusion methods on two publicly available and own polarization datasets, experimental results show that the proposed method outperforms other comparative algorithms in both qualitative comparison and quantitative analysis.

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Section: Related Work 21 Multiscale Transform For Image Fusionmentioning

confidence: 99%