结合FABEMD和改进的显著性检测的图像融合

doi:10.3969/j.issn.1001-506X.2020.02.06

Abstract

Abstract:

Aiming at the problem that the significant targets are not prominent, the contrast is low, and there are many artifacts in infrared and visible image fusion, an image fusion algorithm combining fast and adaptive bidimensional empirical mode decomposition (FABMED) with improved visual saliency detection is proposed. First, the multi-scale decomposition of infrared and visible images is performed by FABEMD to obtain the corresponding base layer and detail layers. A dim suppression improvement is then performed on the maximum symmetric surround saliency detection, which is used for the fusion of the base layer. Combined with the improved saliency detection and guided filter, the detail layers are fused. To this end, the inverse FABEMD transform on each fusion sub-image is performed to reconstruct the fused image. Compared with other typical fusion algorithms, the simulation experiments verify the effectiveness of the proposed algorithm.

Key words: image fusion, fast and adaptive bidimensional empirical mode decomposition (FABEMD), saliency detection, guided filter

CLC Number:

TP391

Ying AN, Xunli FAN, Li CHEN, Pei LIU. Image fusion combining FABEMD with improved saliency detection[J]. Systems Engineering and Electronics, 2020, 42(2): 292-300.

Figures/Tables 9

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Table 1

References 26

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融合图像	融合规则	算法
融合图像	融合规则	本文算法	MSVD	GF	CP	ASR	CBF	CSR	WLS	LatLRR
S1	EN	7.178 9	6.470 0	7.251 8	6.800 5	6.567 3	7.050 6	6.523 6	6.984 7	6.646 8
	SD	50.546 3	23.850 1	44.620 9	31.276 7	26.249 1	40.941 6	25.183 7	39.044 3	26.796 3
	MI	1.246 7	0.881 5	2.790 3	0.584 4	0.811 2	1.417 8	0.854 6	0.928 5	0.831 4
	SSIM	0.728 3	0.770 8	0.718 6	0.713 4	0.743 5	0.621 3	0.769 3	0.727 2	0.777 6
S2	EN	7.097 6	6.259 4	6.377 7	6.833 9	6.441 5	6.548 2	6.433 8	6.602 8	6.623 9
	SD	38.063 0	22.868 4	26.060 9	32.434 7	25.861 4	27.418 4	25.754 8	28.251 3	30.935 2
	MI	1.429 8	0.768 6	0.761 6	0.830 2	0.773 1	0.763 5	0.789 6	0.785 0	0.867 8
	SSIM	0.717 2	0.743 5	0.727 2	0.692 4	0.739 0	0.621 0	0.739 7	0.727 4	0.754 5
S3	EN	7.546 0	6.553 1	6.857 5	6.682 5	6.809 1	6.777 0	6.801 5	6.970 2	6.903 6
	SD	64.279 6	31.619 0	32.898 3	34.143 0	34.497 6	33.193 0	34.386 7	49.308 0	37.729 5
	MI	2.315 8	0.987 0	1.220 2	0.597 7	1.063 4	0.945 2	1.071 4	1.015 8	1.020 2
	SSIM	0.717 3	0.741 1	0.725 6	0.695 1	0.736 8	0.638 5	0.753 3	0.716 6	0.767 1
S4	EN	7.320 1	6.333 5	7.026 9	6.806 8	6.363 9	6.877 8	6.353 5	6.740 3	6.554 4
	SD	57.213 8	27.283 5	48.277 0	37.090 8	28.778 1	36.413 4	28.559 7	42.076 6	32.076 4
	MI	1.718 8	0.956 9	1.734 6	0.695 2	0.891 5	0.933 9	0.967 5	1.008 8	0.939 2
	SSIM	0.700 4	0.714 2	0.688 1	0.640 8	0.698 9	0.539 6	0.718 9	0.697 3	0.732 2
S5	EN	7.593 7	6.777 3	6.857 5	7.035 6	6.410 8	6.869 1	6.801 5	7.236 6	6.572 2
	SD	59.808 7	33.952 5	32.898 3	44.807 0	23.770 4	33.266 5	34.386 7	53.665 0	26.291 3
	MI	1.247 1	0.723 9	0.624 3	1.082 6	1.183 6	0.552 5	0.721 3	0.720 9	1.084 2
	SSIM	0.589 7	0.458 7	0.437 7	0.503 5	0.620 7	0.358 8	0.454 9	0.361 4	0.640 0

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Image fusion combining FABEMD with improved saliency detection

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