Infrared image generation with unpaired training samples

Module	Encoder			Convert		Decoder
Module	Convolution kernel	Network Connection	Number of convolution kernels	Module	Number	Convolution kernel	Network Connection	Number of convolution kernels
Instruction	7×7	Conv ，BN ，ReLU	64	Transformer	1	3×3	ConvTranspose ，BN ，ReLU	128
	3×3	Conv ，BN ，ReLU	128			3×3	ConvTranspose ，BN ，ReLU	64
	3×3	Conv ，BN ，ReLU	256			7×7	Conv ，BN ，ReLU	3

Table 2. Experimental platform configuration
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Table 2. Experimental platform configuration
Names Related configurations
GPU NVIDIA Quadro GV100
CPU Inter Xeon Silver 4210/128 G
GPU Memory size 32 G
Operating systems Win10
Computing platform CUDA11.0

Table 3. Setting of experimental parameters
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Table 3. Setting of experimental parameters
Parameter Epoch Batch size Learning rate $λ_{G A N}$ $λ_{M C}$ $λ_{S S}$ $λ_{I D T}$
Value 300 1 0.000 2 1 2 10 1

Table 4. Comparison of image evaluation indexes

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Table 4. Comparison of image evaluation indexes

Method	Pub’Year	Architectural Scene			Traffic Scene
Method	Pub’Year	PSNR/dB	SSIM	FID	PSNR/dB	SSIM	FID
GLANet	2021	12.720	0.631	75.39	13.823	0.680	68.27
CUT	ECCV’2020	12.895	0.634	71.25	13.995	0.675	62.21
CycleGAN	ICCV’2017	13.138	0.646	66.52	14.779	0.766	50.11
DSMAP	ECCV’2020	14.351	0.738	50.38	15.364	0.780	42.88
UGATIT	ICLR’2019	15.210	0.765	46.92	16.641	0.805	38.57
VTIGAN	Our	15.850	0.796	39.36	16.750	0.818	36.48

Table 5. Results of ablation experiments

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Table 5. Results of ablation experiments

Loss function				PSNR/dB	SSIM	FID
Adversarial loss	Multi-layer contrast loss	Style similarity loss	Identity loss	PSNR/dB	SSIM	FID
√	√	√		15.182	0.721	45.84
√	√		√	10.146	0.513	96.47
√		√	√	9.851	0.494	98.18
√	√	√	√	15.850	0.796	39.36

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Wei CAI, Bo JIANG, Xinhao JIANG, Zhiyong YANG. Infrared image generation with unpaired training samples[J]. Optics and Precision Engineering, 2023, 31(24): 3651

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