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Adaptive arc area inpainting and image enhancement method based on AI-DLC model

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Abstract

Complex industrial scenarios are accompanied by many disturbances, especially when arcs are used as a industrial technique method in the process flow. Arc interference can cause disturbances such as contrast reduction, color deviation, instantaneous overexposure, low illumination, and loss of detail to the captured images, as hinders the development of industry toward the intelligent direction industrial intelligence. Due to the particularity of arc interference, none of the existing studies can be applied to the inpainting of such images. In this study, we constructed the Arc Interference—Distance, Light intensity, and Color (AI-DLC) model by analyzing the characteristics of arc light and its mechanism of interference to the image, which was used to measure the local interference of arc light. Based on this model, we propose the adaptive arc area inpainting and image enhancement method. This method, firstly, splits the original image into several equal-sized patches. Secondly, it classifies them according to the model values. Finally, the patches are processed by each adaptive module. Through experiments in real industrial scenes, compared with commonly used image restoration methods, this method can effectively repair the arc area, enhance image information, and improve image quality.

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Acknowledgements

We would like to thank the reviewers for their help in improving the paper. This work was supported by the Baosteel Technology Research and Development Fund (Grant No. 2021310014000358). This work was supported by Shanghai Collaborative Innovation Technology Fund (Grant No. XTCX2022-29).

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  1. Department of Control Science and Engineering, Shanghai Institute of Technology, 100 Haiquan Road, Shanghai, 201418, China

    Tong Mou & Xiaobin Li

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  1. Tong Mou
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Correspondence to Xiaobin Li.

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Mou, T., Li, X. Adaptive arc area inpainting and image enhancement method based on AI-DLC model. Vis Comput 39, 6151–6165 (2023). https://doi.org/10.1007/s00371-022-02718-5

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