X-ray image fusion for ancient bronze mirror based on intuitionistic fuzzy set entropy measure and salient feature detection

针对被锈蚀覆盖的古铜镜因镜缘与镜心区域厚度不均,单能X射线无法检测出完整的纹饰和病害信息的问题,本文提出一种直觉模糊集熵测度和显著特征检测的古铜镜X光图像融合方法。首先,引入有效引导滤波对高能量X光图像的纹饰结构做对比度增强。接着,采用联合双边滤波和结构-纹理分解策略设计三个尺度分解模型,以提取不同能量X光图像的能量层、残差层和细节层信息。其次,能量层通过<inline-formula><alternatives><math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><msub><mrow><mi>l</mi></mrow><mrow><mn mathvariant="normal">1</mn></mrow></msub></math><graphic specific-use="big" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/44ECF1C3-242E-438a-8C5C-9C95F190D5F8-M001.jpg"><?fx-imagestate width="2.03200006" height="3.21733332"?></graphic><graphic specific-use="small" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/44ECF1C3-242E-438a-8C5C-9C95F190D5F8-M001c.jpg"><?fx-imagestate width="2.03200006" height="3.21733332"?></graphic></alternatives></inline-formula>-<inline-formula><alternatives><math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mi mathvariant="normal">m</mi><mi mathvariant="normal">a</mi><mi mathvariant="normal">x</mi></math><graphic specific-use="big" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/44ECF1C3-242E-438a-8C5C-9C95F190D5F8-M002.jpg"><?fx-imagestate width="5.50333309" height="2.37066650"?></graphic><graphic specific-use="small" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/44ECF1C3-242E-438a-8C5C-9C95F190D5F8-M002c.jpg"><?fx-imagestate width="5.50333309" height="2.37066650"?></graphic></alternatives></inline-formula>规则得到融合后的能量图像,残差层利用直觉模糊集熵测度构造小尺度纹理特征融合模块,细节层结合扩展差分高斯与空间频率增强算子构建复合型显著特征检测策略。最后,将能量融合图、残差融合图和细节融合图相加得到最终融合结果。实验结果表明,本文方法的6种客观评价指标AG,SF,SD,SCD,<inline-formula><alternatives><math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><msup><mrow><mi>N</mi></mrow><mrow><mi mathvariant="normal">A</mi><mi mathvariant="normal">B</mi><mo>/</mo><mi mathvariant="normal">F</mi></mrow></msup></math><graphic specific-use="big" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/44ECF1C3-242E-438a-8C5C-9C95F190D5F8-M003.jpg"><?fx-imagestate width="7.45066643" height="2.70933342"?></graphic><graphic specific-use="small" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/44ECF1C3-242E-438a-8C5C-9C95F190D5F8-M003c.jpg"><?fx-imagestate width="7.45066643" height="2.70933342"?></graphic></alternatives></inline-formula>和SSIM相较于对比方法分别平均提高了23.59%,22.99%,16.12%,42.55%,17.07%,20.54%,融合结果可以有效保留古铜镜清晰的纹饰细节和病害裂隙的关键特征,在对比度和结构保持等方面都优于其他对比方法。

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Publication Details

Journal
Optics and Precision Engineering
Published
2025-01-01
DOI
https://doi.org/10.37188/ope.20253302.0262
Primary Topic
Advanced Image Fusion Techniques
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article
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article

X-ray image fusion for ancient bronze mirror based on intuitionistic fuzzy set entropy measure and salient feature detection

Zengguo Sun, Q.P. Zhang, Meng Wu, Guo Ge et al.
Optics and Precision Engineering
Advanced Image Fusion Techniques
article

X-ray image fusion for ancient bronze mirror based on intuitionistic fuzzy set entropy measure and salient feature detection

Zengguo Sun, Q.P. Zhang, Meng Wu, Guo Ge, Jinjuan Xiang
article en

Abstract

针对被锈蚀覆盖的古铜镜因镜缘与镜心区域厚度不均,单能X射线无法检测出完整的纹饰和病害信息的问题,本文提出一种直觉模糊集熵测度和显著特征检测的古铜镜X光图像融合方法。首先,引入有效引导滤波对高能量X光图像的纹饰结构做对比度增强。接着,采用联合双边滤波和结构-纹理分解策略设计三个尺度分解模型,以提取不同能量X光图像的能量层、残差层和细节层信息。其次,能量层通过<inline-formula><alternatives><math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><msub><mrow><mi>l</mi></mrow><mrow><mn mathvariant="normal">1</mn></mrow></msub></math><graphic specific-use="big" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/44ECF1C3-242E-438a-8C5C-9C95F190D5F8-M001.jpg"><?fx-imagestate width="2.03200006" height="3.21733332"?></graphic><graphic specific-use="small" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/44ECF1C3-242E-438a-8C5C-9C95F190D5F8-M001c.jpg"><?fx-imagestate width="2.03200006" height="3.21733332"?></graphic></alternatives></inline-formula>-<inline-formula><alternatives><math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mi mathvariant="normal">m</mi><mi mathvariant="normal">a</mi><mi mathvariant="normal">x</mi></math><graphic specific-use="big" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/44ECF1C3-242E-438a-8C5C-9C95F190D5F8-M002.jpg"><?fx-imagestate width="5.50333309" height="2.37066650"?></graphic><graphic specific-use="small" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/44ECF1C3-242E-438a-8C5C-9C95F190D5F8-M002c.jpg"><?fx-imagestate width="5.50333309" height="2.37066650"?></graphic></alternatives></inline-formula>规则得到融合后的能量图像,残差层利用直觉模糊集熵测度构造小尺度纹理特征融合模块,细节层结合扩展差分高斯与空间频率增强算子构建复合型显著特征检测策略。最后,将能量融合图、残差融合图和细节融合图相加得到最终融合结果。实验结果表明,本文方法的6种客观评价指标AG,SF,SD,SCD,<inline-formula><alternatives><math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><msup><mrow><mi>N</mi></mrow><mrow><mi mathvariant="normal">A</mi><mi mathvariant="normal">B</mi><mo>/</mo><mi mathvariant="normal">F</mi></mrow></msup></math><graphic specific-use="big" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/44ECF1C3-242E-438a-8C5C-9C95F190D5F8-M003.jpg"><?fx-imagestate width="7.45066643" height="2.70933342"?></graphic><graphic specific-use="small" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/44ECF1C3-242E-438a-8C5C-9C95F190D5F8-M003c.jpg"><?fx-imagestate width="7.45066643" height="2.70933342"?></graphic></alternatives></inline-formula>和SSIM相较于对比方法分别平均提高了23.59%,22.99%,16.12%,42.55%,17.07%,20.54%,融合结果可以有效保留古铜镜清晰的纹饰细节和病害裂隙的关键特征,在对比度和结构保持等方面都优于其他对比方法。

Optics and Precision EngineeringVol. 33(2)
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Advanced Image Fusion Techniques
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