基于NSST的改进最大最小滤波与DCT-LSF的多聚焦图像融合
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1.西安建筑科技大学机电工程学院,西安 710055;2.陕西省机械研究院,陕西 咸阳 712200

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TP3

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科技创新基地—科技资源开放共享平台(S2020-ZC-PT-0030)


Multi-focus image fusion based on improved Maximum and Minimum filter and DCT-LSF
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1.School of Mechanical and Electrical Engineering, Xi’an University of Architectural Science and Technology, Xi’an 710055, China; 2.Shaanxi Provincial Machinery Research Institute, Xianyang712000, China

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    摘要:

    针对图像采集传感器的景深有限,导致采集图像的局部区域产生的失焦现象,本文提出了一种新的多聚焦图像融合算法。在NSST的框架下,对低频子带分解系数采用基于离散余弦变换(DCT)和局部空间频率(LSF)的融合规则;对高频子带分解系数则采用基于最大最小滤波结合平均滤波、中值滤波(MMAM)的融合规则;然后进行INSST重构获得融合图像。实验结果表明,与经典图像融合算法相比较,本文算法能有效融合图像的高低频子带信息,并在主客观评价方面都达到了较好的效果。

    Abstract:

    In view of the limited depth of field of the image acquisition sensor, which leads to the out-of-focus phenomenon in the local area of the acquired image, this paper proposes a new multi-focus image fusion algorithm. Under the framework of NSST, the fusion rules based on discrete cosine transform (DCT) and local spatial frequency (LSF) are used for the low-frequency sub-band decomposition coefficients, and the fusion rules based on the maximum and minimum filtering combined with average filtering and median filtering (MMAM) are used for the high-frequency sub-band decomposition coefficients; and then perform INSST reconstruction to obtain fused images. The experimental results show that, compared with the classical image fusion algorithm, the proposed algorithm can effectively fuse the high and low frequency sub-band information of the image, and achieves better results in both subjective and objective evaluation.

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贺腾飞,贺利乐,高党国.基于NSST的改进最大最小滤波与DCT-LSF的多聚焦图像融合[J].电子测量技术,2022,45(22):99-105

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  • 在线发布日期: 2024-03-19
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