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Detail-Preserving Multi-exposure Fusion for DR Images of Turbine Blades with Local Contrast Analysis and Exposure Intensity

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Abstract

Digital radiographic imaging technique based on the Digital Detector Array (DDA) plays an essential role in the non-destructive testing of turbine blades. However, the Digital radiography (DR) of turbine blade obtained by the X-ray spectrum at the single exposure parameter cannot provide effective information feedback to the whole blade in digital radiographic testing. Aiming at this critical issue, a detail-preserving fusion method of multi-exposure sequence DR images is presented in this paper. Firstly, the unordered DR images are divided into three categories of over-exposure, normal-exposure, and under-exposure by using clustering method. Then, the weight design methods based on local contrast analysis and exposure intensity are employed to accomplish the initial weight fusion. The guided filter and normalization are utilized to ensure the weight maps are edge-preserving and smoothing. Finally, the refined weight maps and initial DR images are decomposed by multiscale pyramid. The fused DR image can be reconstructed by the new fusion pyramid with satisfying visual effects. Four different experimental results show that the fused blade DR image can clearly show the full internal features of turbine blades, which integrally covers the advantages of high-exposure images and low-exposure images. Compared with the four existing DR fusion methods currently in use, the fusion effect of proposed method is most significantly improved. For four diverse kinds of turbine blades, all the fused images obtained by our method have the highest structural-similarity metric and the second shortest execution time.

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Funding

The authors are grateful for the financial support provided by the Fundamental Research Funds for the Central Universities (grant number xhj032021006-05), the Key Research and Dvelopment Program of Shaanxi (grant number 2023-YBGY-397).

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Authors and Affiliations

  1. State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an, 710049, Shaanxi, People’s Republic of China

    Lei Zhang, Bing Li, Lei Chen, Xiang Wei & Zhongyu Shang

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  1. Lei Zhang
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  2. Bing Li
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  3. Lei Chen
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  4. Xiang Wei
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  5. Zhongyu Shang
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Contributions

LZ: Data Curation, methodology, validation, writing—original draft; BL: Resources, review, supervision; LC: Review and editing, investigation; WX: Review and editing, supervision; ZS: Review and conceptualization, supervision.

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Correspondence to Lei Chen.

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Zhang, L., Li, B., Chen, L. et al. Detail-Preserving Multi-exposure Fusion for DR Images of Turbine Blades with Local Contrast Analysis and Exposure Intensity. J Nondestruct Eval 42, 98 (2023). https://doi.org/10.1007/s10921-023-01008-x

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