Infrared and visible image perceptive fusion through multi-level Gaussian curvature filtering image decomposition

Tan, Wei; Zhou, Huixin; Song, Jiangluqi; Li, Huan; Yu, Yue; Du, Juan

doi:10.1364/ao.58.003064

Cited by 65 publications

(26 citation statements)

References 39 publications

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“…Meanwhile, some fine edges will be smoothed off [15]. According to this feature, a novel hybrid decomposition model is produced [16]- [18]. An EPF and Gaussian filtering (GF) is used in this model.…”

Section: Introductionmentioning

confidence: 99%

“…Using the edge-preserving characteristic of EPF and the smoothing characteristic of GF, the input image is decomposed into three layers: small-scale layer, large-scale layer, and base layer. Classical EPF-GF decomposition methods include Bilateral-Gaussian filtering decomposition (BGFD) [16], Rolling guidance-Gaussian filtering decomposition (CGGFD) [17], and Curvature-Gaussian filtering decomposition (CGFD) [18]. This hybrid decomposition model has obtained great success in image fusion.…”

Section: Introductionmentioning

confidence: 99%

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Remote Sensing Image Fusion via Boundary Measured Dual-Channel PCNN in Multi-Scale Morphological Gradient Domain

et al. 2020

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In this paper, a remote sensing image fusion method based on boundary measured dual-channel pulse-coupled neural network (PCNN) in multi-scale morphological gradient (MSMG) domain is proposed. Firstly, the panchromatic (PAN) image is decomposed into three parts, a small-scale image, a large-scale image, and a base image through a co-occurrence filtering (CoF)-based decomposition model. Secondly, an HSI transform is applied in the multispectral (MS) image to obtained intensity, hue and saturation components. Thirdly, a PCNN fusion strategy modulated by MSMG is used to fuse the base image and the intensity component of the MS image. Then, a fused intensity image is obtained by combining the small-scale image, large-scale image and the fused approximate image. Finally, the final fused image can be reconstructed by an inverse HSI transform. Experiments in four datasets demonstrate that the proposed method obtains the best performance in most cases. INDEX TERMS Remote sensing image fusion, co-occurrence filtering, multi-scale morphological gradient, pulse-coupled neural network.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Remote Sensing Image Fusion via Boundary Measured Dual-Channel PCNN in Multi-Scale Morphological Gradient Domain

et al. 2020

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“…With the gradual deepening of research on single-and dual-channel PCNN, the method is becoming one of the most popular method in the image fusion. Compared with the wavelet transform [1] that has been applied to multi-focus image fusion, FSD [2] and Gradient pyramid [3], PCNN is still a research focus of multi-focus image fusion, medical image fusion, and the well-known works are [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

“…There are many scholars concerned about the combination of PCNN model and other models, which can foster strengths and circumvent weaknesses, and achieve the best fusion effect. Such as the combination of wavelet transform and other networks is proposed in the literature [16][17]. We first decompose the infrared and visible image into multi-direction and multiscale coefficient matrices by RLNSST decomposition .…”

Section: Introductionmentioning

confidence: 99%

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A Novel Medical Image Fusion Method Using Multi-Channel Pulse Coupled Neural Networks

Zhao

2020

IEEE Access

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A novel method for multi-channel pulse coupled neural networks (PCNN) is proposed in order to overcome the difficulty in lack of M-PCNN and question of many images fusion and instability of double-channel PCNN due to the different fusion sequence. In this study a novel multi-channel pulse coupled neural networks is analyzed in detail from odd numbers channel PCNN and even numbers channel PCNN. Through a combination of odd numbers channel PCNN and even numbers channel PCNN this study can realize image fusion by M-PCNN. A novel calculation method about linkingweight based on gray energy is proposed in order to overcome the lack of linking-weight in M-PCNN. To effectively avoid the Laplasse operator and calculation repeatedly, this study can realize not only many images fusion, but also better time efficiency and less information loss. Experimental results show that the presented method outperforms existing methods, in both fusion effect and different performance evaluation criteria.

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Infrared and Visual Image Fusion via Multi-modal Decomposition and PCNN in Gradient Domain Fusion Measure

Tan

Zhang

Qian

et al. 2020

Lecture Notes in Computer Science

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Infrared and visible image perceptive fusion through multi-level Gaussian curvature filtering image decomposition

Cited by 65 publications

References 39 publications

Remote Sensing Image Fusion via Boundary Measured Dual-Channel PCNN in Multi-Scale Morphological Gradient Domain

Remote Sensing Image Fusion via Boundary Measured Dual-Channel PCNN in Multi-Scale Morphological Gradient Domain

A Novel Medical Image Fusion Method Using Multi-Channel Pulse Coupled Neural Networks

Infrared and Visual Image Fusion via Multi-modal Decomposition and PCNN in Gradient Domain Fusion Measure

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