基于引导滤波与双树复小波变换的红外与可见光图像融合

Jiang Mai, 姜迈; Sha Guijun, 沙贵君; Li Ning, 李宁

doi:10.3788/lop212755

激光与光电子学进展

2023

DOI: 10.3788/lop212755

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基于引导滤波与双树复小波变换的红外与可见光图像融合

姜迈 Jiang Mai,

沙贵君 Sha Guijun,

李宁 Li Ning

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2024

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Cited by 2 publications

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基于显著性自适应权重图的红外可见光图像融合

Ding Haiyang,

Dong Mingli,

Liu Chenhua

et al. 2024

激光与光电子学进展

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To solve the problem of insufficient use of source image information by existing fusion methods, a method is proposed using rolling guided filter and anisotropic diffusion to extract the base and detail layers of an image, respectively. These layers were then fused using visual saliency mapping and weight map construction, and a certain weight was added to merge the fused layers into the final image. The proposed method was tested and verified using several scenes from an open dataset. The experimental results show that the final images obtained using the proposed method exhibit better contrast, retain richer texture features at edge details, and maintain a uniform image pixel intensity distribution; furthermore, the visual effects and fusion accuracy of the final images are better than other existing fusion methods. Moreover, significant progress has been made in indicators, such as average gradient, information entropy, and spatial frequency.

show abstract

基于显著性自适应权重图的红外可见光图像融合

Ding Haiyang,

Dong Mingli,

Liu Chenhua

et al. 2024

激光与光电子学进展

View full text Add to dashboard Cite

show abstract

红外与可见光图像融合：统计分析，深度学习方法和未来展望

Wu Yifei,

Yang Rui,

Qishen Lü

et al. 2024

Laser Optoelectron. Prog.

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基于引导滤波与双树复小波变换的红外与可见光图像融合

Cited by 2 publications

References 19 publications

基于显著性自适应权重图的红外可见光图像融合

基于显著性自适应权重图的红外可见光图像融合

红外与可见光图像融合：统计分析，深度学习方法和未来展望

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