Infrared and Visible Image Fusion in Latent Low Rank Representation Framework Based on Convolution Neural Network and Guided Filtering

Xicheng LOU; Xin FENG

doi:10.3788/gzxb20215003.0310004

Acta Photonica Sinica, Volume. 50, Issue 3, 180(2021)

Infrared and Visible Image Fusion in Latent Low Rank Representation Framework Based on Convolution Neural Network and Guided Filtering

Xicheng LOU and Xin FENG

Author Affiliations

Key Laboratory of Manufacturing Equipment Mechanism Design and Control of Chongqing， College of Mechanical Engineering， Chongqing Technology and Business University， Chongqing400067， China

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Figures & Tables(11)

Fig. 1. LatLRR decomposition results

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Fig. 2. Neuron structure

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Fig. 3. Siamese network structure

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Fig. 4. CNN processing diagram

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Fig. 5. GF processing diagram

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Fig. 6. The framework of proposed method

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Fig. 7. Comparison of the first set of infrared and visible image fusion results

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Fig. 8. Comparison of the second set of infrared and visible image fusion results

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Fig. 9. Comparison of the third set of infrared and visible image fusion results

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Fig. 10. Comparison of the fourth set of infrared and visible image fusion results

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Table 1. Objective evaluation results of different fusion methods

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Table 1. Objective evaluation results of different fusion methods

Image	Fusion method	$Q_{M I}$	$Q_{P}$	$Q_{S}$	$Q_{C B}$
First set of fusion results	Proposed method	0.374 1	0.416 8	0.789 4	0.589 6
	Ref. [17]	0.285 0	0.324 4	0.796 1	0.582 4
	Ref. [15]	0.257 0	0.250 6	0.803 8	0.519 1
	SR	0.298 7	0.167 7	0.726 2	0.552 0
	DTCWT	0.223 1	0.273 1	0.790 8	0.549 8
	CVT	0.210 4	0.220 7	0.780 4	0.528 6
	NSCT_SR	0.232 2	0.251 7	0.784 2	0.564 0
Second set of fusion results	Proposed method	0.460 3	0.435 8	0.784 6	0.550 5
	Ref. [17]	0.347 5	0.477 0	0.810 7	0.522 6
	Ref. [15]	0.280 6	0.336 1	0.783 8	0.466 9
	SR	0.392 6	0.234 2	0.704 6	0.503 2
	DTCWT	0.237 8	0.428 7	0.804 0	0.456 9
	CVT	0.224 5	0.359 1	0.796 3	0.455 5
	NSCT_SR	0.307 1	0.383 5	0.781 3	0.462 2
Third set of fusion results	Proposed method	0.399 5	0.263 1	0.844 9	0.466 5
	Ref. [17]	0.335 2	0.255 7	0.824 6	0.486 2
	Ref. [15]	0.218 2	0.230 6	0.801 7	0.461 1
	SR	0.444 4	0.117 3	0.780 9	0.494 1
	DTCWT	0.185 6	0.212 7	0.845 5	0.487 8
	CVT	0.163 3	0.184 6	0.830 6	0.477 1
	NSCT_SR	0.498 3	0.256 6	0.842 2	0.520 6
Fourth set of fusion results	Proposed method	0.358 1	0.414 5	0.882 1	0.474 4
	Ref. [17]	0.378 5	0.509 6	0.764 2	0.553 1
	Ref. [15]	0.333 9	0.389 0	0.801 8	0.511 6
	SR	0.512 9	0.382 4	0.796 3	0.579 4
	DTCWT	0.502 4	0.331 5	0.788 0	0.548 3
	CVT	0.283 3	0.414 8	0.847 3	0.508 5
	NSCT_SR	0.466 4	0.435 5	0.870 9	0.564 1

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Xicheng LOU, Xin FENG. Infrared and Visible Image Fusion in Latent Low Rank Representation Framework Based on Convolution Neural Network and Guided Filtering[J]. Acta Photonica Sinica, 2021, 50(3): 180

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Paper Information

Category: Image Processing

Received: --

Accepted: --

Published Online: Jul. 13, 2021

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DOI:10.3788/gzxb20215003.0310004

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology