A Dual Branch Edge Convolution Fusion Network for Infrared and Visible Images

Hongde ZHANG; Xin FENG; Jieming YANG; Guohang QIU

doi:10.3788/gzxb20245308.0810004

Journals >Acta Photonica Sinica >Volume 53 >Issue 8 >Page 0810004 > Article

Acta Photonica Sinica
Vol. 53, Issue 8, 0810004 (2024)

A Dual Branch Edge Convolution Fusion Network for Infrared and Visible Images

Hongde ZHANG, Xin FENG^*, Jieming YANG, and Guohang QIU

Author Affiliations

School of Mechanical Engineering， Chongqing Key Laboratory of Green Design and Manufacturing of Intelligent Equipment， Chongqing Technology and Business University， Chongqing 400067， China

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DOI: 10.3788/gzxb20245308.0810004 Cite this Article

Hongde ZHANG, Xin FENG, Jieming YANG, Guohang QIU. A Dual Branch Edge Convolution Fusion Network for Infrared and Visible Images[J]. Acta Photonica Sinica, 2024, 53(8): 0810004 Copy Citation Text

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Fig. 1. Infrared and visible image fusion based on dual-branch convolution network

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Fig. 2. Edge convolution block

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Fig. 3. Convolutional block attention module

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Fig. 4. Results of infrared and visible image fusion with different modules

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Fig. 5. Results of infrared and visible image fusion with different methods

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Fig. 6. Line chart of evaluation indexes with different methods

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Fig. 7. Radar chart of evaluation indexes with different methods

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Methods	MSE	SF	CC	PSNR	$Q_{A B}^{F}$	MS-SSIM
Baseline	0.068 9	0.153 4	0.517 0	0.062 3	0.763 1	0.000 0
Without ECB	0.594 5	0.204 5	0.721 0	0.558 2	0.247 0	0.709 7
Without CBAM	0.415 8	0.877 4	0.333 1	0.393 4	0.465 6	0.895 4
DBECFuse	1.000 0	0.352 0	0.800 5	1.000 0	0.613 5	0.980 3

Table 1. Mean of normalized evaluation indexes with different modules

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Params	MSE	SF	CC	PSNR	$Q_{A B}^{F}$	MS-SSIM
$α = 0.5, μ = 0.1$	0.489 5	0.082 1	0.416 2	0.479 3	0.774 6	0.215 7
$α = 0.5, μ = 1.0$	0.507 9	0.108 2	0.570 2	0.495 5	0.595 8	0.301 4
$α = 0.5, μ = 10$	0.419 2	0.085 8	0.550 3	0.403 6	0.997 1	0.126 2
$α = 1.0, μ = 0.1$	0.520 4	0.078 8	0.563 5	0.506 8	0.534 4	0.384 8
$α = 1.0, μ = 1.0$	0.522 9	0.089 6	0.638 2	0.507 6	0.516 3	0.416 8
$α = 1.0, μ = 10$	0.420 5	0.067 6	0.604 1	0.402 9	0.841 6	0.136 8
$α = 5.0, μ = 0.1$	0.364 5	1.000 0	0.328 6	0.349 9	0.154 1	0.920 3
$α = 5.0, μ = 1.0$	0.959 9	0.082 7	0.942 5	0.951 9	0.010 0	0.880 4
$α = 5.0, μ = 10$	0.717 6	0.104 0	0.836 0	0.694 4	0.306 3	0.749 4

Table 2. Mean of normalized evaluation indexes with different params

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Methods	MSE	SF	CC	PSNR	$Q_{A B}^{F}$	MS-SSIM
MST-SR	0.676 1	0.775 9	0.702 6	0.633 7	0.050 1	0.710 1
GTF	0.307 6	0.196 6	0.172 9	0.252 2	0.687 9	0.400 3
NestFuse	0.527 9	0.530 6	0.679 0	0.447 4	0.099 2	0.783 0
Res2NetFuse	0.571 9	0.363 5	0.653 9	0.491 2	0.186 5	0.651 3
SwinFuse	0.233 5	0.559 0	0.441 7	0.204 8	0.676 3	0.237 1
SeAFusion	0.239 5	0.820 5	0.746 9	0.182 1	0.063 7	0.708 5
PIAFusion	0.379 8	0.611 0	0.672 3	0.301 3	0.131 5	0.727 2
FECFusion	0.452 6	0.706 8	0.663 7	0.374 3	0.248 4	0.751 8
DBECFuse	0.904 6	0.373 7	0.997 2	0.877 3	0.494 5	1.000 0

Table 3. Mean of normalized evaluation indexes with different methods

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Methods	Params	FLOPs
NestFuse	2.732 8 M	152.422 7 G
Res2NetFuse	89.617 K	18.549 3 G
SwinFuse	336.193 K	67.439 4 G
SeAFusion	166.657 0 K	21.758 0 G
PIAFusion	1.176 1 M	154.023 2 G
FECFusion	144.453 K	18.772 1 G
DBECFuse	33.317 K	8.907 1 G

Table 4. Params and FLOPs with different methods

Hongde ZHANG, Xin FENG, Jieming YANG, Guohang QIU. A Dual Branch Edge Convolution Fusion Network for Infrared and Visible Images[J]. Acta Photonica Sinica, 2024, 53(8): 0810004

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