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Multi-exposure fusion for welding region based on multi-scale transform and hybrid weight

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Abstract

The multi-exposure fusion is an effective image enhancement technique for high dynamic range (HDR) scene. In this paper, a novel multi-scale hybrid weight fusion framework is proposed to overcome the inherent defects of detail loss during the reconstruction process. Firstly, a novel hybrid weight method is developed by employing the local weight of a single image, the global weight between different exposure images, and the saliency weight from spectral residual model. Secondly, a new multi-scale hybrid weight image fusion algorithm based on Laplacian pyramid is proposed by applying the hybrid weight at each scale. The advantages of the proposed fusion algorithm over individual weight are analyzed from a theoretical point of view and then experimentally verified with multi-exposure image in welding region. Furthermore, the guided filter is utilized to smooth the reconstruction image, Laplacian pyramid image, and saliency weight maps for all the low dynamic range (LDR) images, which can effectively keep the edge information and reduce artifacts of weld seam region. Finally, by comparing our results comprehensively with other methods subjectively and objectively, the proposed fusion framework is verified that it can obtain better performance.

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Funding

This work was supported in part by National Natural Science Foundation (NNSF) of China under Grant 61873315, 61403119, Natual Science Foundation of Hebei Province under Grant F201402166, F2018202078, Special Correspondent Technology Plan of Tianjin under Grant 15JCTPJC55500, Science and Technology Project of Hebei Province under Grant 17211804D, and Talent Support Project in Hebei Province.

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Authors and Affiliations

  1. School of Artificial Intelligence, Hebei University of Technology, Tianjin, 300130, People’s Republic of China

    Haiyong Chen, Yafei Ren, Junqi Cao, Weipeng Liu & Kun Liu

Authors
  1. Haiyong Chen
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  2. Yafei Ren
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  3. Junqi Cao
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  4. Weipeng Liu
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  5. Kun Liu
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Corresponding author

Correspondence to Kun Liu.

Additional information

We believe that three aspects of the work will make it interesting to general readers at least.

1) A novel multi-scale hybrid weight fusion framework is proposed to overcome the inherent defects of detail loss during reconstruction process, which is based on Laplacian pyramid and the hybrid weight including the local weight, the global weight considering the gradient vectors between different exposure images, and saliency weight based on spectral residual model.

2) The advantages of the proposed fusion algorithm over individual weight are first exhibited in detail from a theoretical point of view, and then experimental results demonstrate that the proposed fusion framework can obtain state-of-the-art performance in multi-exposure image for welding region.

3) The proposed method can effectively alleviate the strong interference of welding process to obtain clear and high-quality images of welding region for many existing cameras with low dynamic range in the industrial application, which help to ensure quality and productivity in the weld process. The method results are given in our manuscript.

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Chen, H., Ren, Y., Cao, J. et al. Multi-exposure fusion for welding region based on multi-scale transform and hybrid weight. Int J Adv Manuf Technol 101, 105–117 (2019). https://doi.org/10.1007/s00170-018-2723-1

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  • DOI: https://doi.org/10.1007/s00170-018-2723-1

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