Infrared and Visible Image Fusion Algorithm Based on Feature Optimization and GAN

n：number of chameleons in search space
d：spatial dimension
t：number of iterations，T：max-number of iterations
Build adaptive optimization image： $I_{o} = y I_{B} + I_{D}$
Design objective function for optimizing parameter y
$F = m i n (L_{A G} + L_{S D} + λ L_{C O N})$
Initialize a population of n chameleons within the search space
$y^{i} = l_{j} + r \times (u_{j} - l_{j})$
Evaluate the fitness of each chameleon using the objective function F
While（t < T）do
Step1：Search for prey
for i=1 to n do
for j=1 to d do
if $r_{i} \geq P_{p}$ then
$y_{t}^{i, j} + p_{1} (p_{t}^{i, j} - G_{_{t}}^{j}) r_{2} + p_{2} (G_{_{t}}^{j} - y_{t}^{i, j}) r_{1}$
else
$y_{t}^{i, j} + μ \cdot l_{b}^{j} s g n (r a n d - 0.5) + μ [(u_{j} - l_{j}) r_{3}] s g n (r a n d - 0.5)$
end if
end for
end for
Step2：Eyes’ rotation reveals prey
for i=1 to n do
$y_{t + 1}^{i} = m \times (y_{t}^{i} - {\bar{y}}_{t}^{i}) + {\bar{y}}_{t}^{i}$
end for
Step3：Hunting process for prey
for i=1 to n do
for j=1 to d do
$y_{t + 1}^{i, j} = y_{t}^{i, j} + [(v_{t}^{i, j})^{2} - (v_{t - 1}^{i, j})^{2}] / (2 a)$
end for
end for
Evaluate the new positions of the chameleons
Update the position of the chameleons
Evaluate the fitness of each chameleon
Return best solution y
t = t + 1
end while
Obtain the global optimal solution y，and then obtain the optimized image I_o

Table 2. Training process of network model

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Table 2. Training process of network model

step1：for M epochs do
step2：for p times do
step3：select b visible image samples： ${I_{v i s}^{1}, I_{v i s}^{2}, \dots, I_{v i s}^{b}}$ ；
step4：select b infrared image samples： ${I_{i r}^{1}, I_{i r}^{2}, \dots, I_{i r}^{b}}$ ；
step5：select b fusion image samples： ${I_{f u s e d}^{1}, I_{f u s e d}^{2}, \dots, I_{f u s e d}^{b}}$ ；
step6：Using the Adam optimizer to update discriminator parameters： $\nabla_{D} (L_{D} = L_{D - i r} + L_{D - v i s})$ ；
step7：end for
step8：select b visible image samples： ${I_{v i s}^{1}, I_{v i s}^{2}, \dots, I_{v i s}^{b}}$ ；
step9：select b infrared image samples： ${I_{i r}^{1}, I_{i r}^{2}, \dots, I_{i r}^{b}}$ ；
step10：Using the Adam optimizer to update generator parameters： $\nabla_{G} (L_{G} = L_{a d v} + λ_{1} L_{c o n t e n t})$ ；
step11：end for

Table 3. Average running time of different algorithms（units：s）
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Table 3. Average running time of different algorithms（units：s）
Algorithms
DenseFuse
FusionGAN
ResNet-ZCA
MDLatLRR
PMGI
RFN-Nest
Ours
Time
0.124
1.846
1.719
5.846
0.637
0.284
0.451 9

Table 4. Ablation experiments objectively results comparison

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Table 4. Ablation experiments objectively results comparison

Module A	Module B	Module C	EN	SF	JE	VIF	SSIM	Q^AB/F
			6.389 5	5.864 2	11.956 1	0.664 2	0.813 4	0.307 0
√			6.415 8	6.468 5	11.990 7	0.693 4	0.824 6	0.350 9
	√		6.625 8	7.130 1	12.128 4	0.691 5	0.826 9	0.377 1
		√	6.489 6	6.256 1	12.034 2	0.689 8	0.819 8	0.315 9
√	√		6.842 9	7.956 0	12.362 9	0.715 3	0.863 7	0.388 0
√		√	6.559 3	6.594 7	12.186 3	0.708 8	0.820 4	0.375 9
	√	√	6.952 4	8.556 3	12.461 2	0.833 5	0.822 5	0.411 8
√	√	√	7.168 8	10.006 5	12.650 9	0.868 6	0.854 2	0.457 4

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Shuai HAO, Jiahao LI, Xu MA, Tian HE, Siyan SUN, Tong LI. Infrared and Visible Image Fusion Algorithm Based on Feature Optimization and GAN[J]. Acta Photonica Sinica, 2023, 52(12): 1210004

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