Image Fusion Based on Residual Learning and Visual Saliency Mapping

Luoyi Ding; Jin Duan; Yu Song; Yong Zhu; Xiaoshan Yang

doi:10.3788/LOP57.161008

Laser & Optoelectronics Progress, Volume. 57, Issue 16, 161008(2020)

Image Fusion Based on Residual Learning and Visual Saliency Mapping

Luoyi Ding¹, Jin Duan^1,2、*, Yu Song¹, Yong Zhu³, and Xiaoshan Yang¹

¹College of Electronic Information Engineering, Changchun University of Science and Technology, Changchun, Jilin 130022, China

²Fundamental Science on Space-Ground Laser Communication Technology Laboratory, Changchun University of Science and Technology, Changchun, Jilin 130044, China

³College of Computer Science and Technology, Changchun University of Science and Technology, Changchun, Jilin 130022, China

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Figures & Tables(8)

Fig. 1. Fusion framework of visible light image and infrared image

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Fig. 2. Mapping relationship between α and weighting coefficient

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Fig. 3. Deep learning fusion framework for edge layer and texture layer

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Fig. 4. Fusion results of Steamboat source images by different methods. (a) Infrared image; (b) visible light image; (c) CBF; (d) WLS; (e) HMSD; (f) JSR; (g) JSRD; (h) CSR; (i) proposed method

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Fig. 5. Fusion results of Street source images by different methods. (a) Infrared image; (b) visible light image; (c) CBF; (d) WLS; (e) HMSD; (f) JSR; (g) JSRD; (h) CSR; (i) proposed method

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Fig. 6. Fusion results of Marne source images by different methods. (a) Infrared image; (b) visible light image; (c) CBF; (d) WLS; (e) HMSD; (f) JSR; (g) JSRD; (h) CSR; (i) proposed method

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Table 1. FMIdct, FMIw, Nabf, and SSIM values of the fused images Steamboat, Street, and Marne

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Table 1. FMIdct, FMIw, Nabf, and SSIM values of the fused images Steamboat, Street, and Marne

Picture name	Metric	CBF	WLS	HMSD	JSR	JSRD	CSR	Proposed method
Steamboat	FMIdct	0.2192	0.3446	0.3379	0.1795	0.1489	0.3547	0.3879
	FMIw	0.2462	0.3636	0.3415	0.1812	0.1483	0.3044	0.3773
	Nabf	0.5448	0.1938	0.1583	0.2552	0.4102	0.0207	0.0139
	SSIM	0.5828	0.8577	0.8382	0.7420	0.6894	0.8751	0.8836
Street	FMIdct	0.2846	0.3492	0.3574	0.2485	0.2279	0.3760	0.3673
	FMIw	0.2486	0.3575	0.3453	0.2769	0.2642	0.3220	0.3802
	Nabf	0.4870	0.1352	0.1299	0.1804	0.1908	0.0220	0.0190
	SSIM	0.4986	0.6709	0.6565	0.6299	0.6237	0.6747	0.6854
Marne	FMIdct	0.1835	0.3236	0.3092	0.1591	0.1324	0.2803	0.3569
	FMIw	0.2407	0.3773	0.3416	0.2266	0.2078	0.3441	0.4254
	Nabf	0.5278	0.1150	0.1636	0.2109	0.2801	0.0193	0.0155
	SSIM	0.4575	0.6769	0.6956	0.6253	0.5808	0.7218	0.7199

Table 2. Mean values of FMIdct, FMIw, Nabf, and SSIM for twenty-one pairs of images

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Table 2. Mean values of FMIdct, FMIw, Nabf, and SSIM for twenty-one pairs of images

Metric	CBF	WLS	HMSD	JSR	JSRD	CSR	Proposed method
FMIdct	0.2631	0.3349	0.3319	0.1644	0.1444	0.3464	0.3762
FMIw	0.3235	0.3803	0.3690	0.2083	0.1842	0.3836	0.4183
Nabf	0.3173	0.2232	0.1543	0.2393	0.3510	0.0196	0.0162
SSIM	0.5996	0.7194	0.7221	0.6064	0.5410	0.7422	0.7714

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Luoyi Ding, Jin Duan, Yu Song, Yong Zhu, Xiaoshan Yang. Image Fusion Based on Residual Learning and Visual Saliency Mapping[J]. Laser & Optoelectronics Progress, 2020, 57(16): 161008

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Paper Information

Category: Image Processing

Received: Dec. 3, 2019

Accepted: Jan. 6, 2020

Published Online: Aug. 5, 2020

The Author Email: Jin Duan (duanjin@vip.sina.com)

DOI:10.3788/LOP57.161008

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology