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A novel generative adversarial net for calligraphic tablet images denoising

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Abstract

Chinese calligraphic images have important historical and artistic value, but natural weathering and man-made decay severely damage these works, thus image denoising is an important topic to be addressed. Traditional denoising methods still leave room for improvement. In this paper, image denoising is modeled as generation of clean image by using GAN (Goodfellow I et al. Advances in Neural Information Processing Systems 2672–2680, 2014) with an embedment of residual dense blocks (Zhang Y et al. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018) that was formerly used for super resolution reconstruction. Meanwhile, a new type of noise is defined to simulate the real noise, and is used for compensation of unpaired data in the training set for GAN. The new structure, used with some preprocessing and training methods, yield satisfactory results compared to known denoising methods.

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Acknowledgements

This work is supported by the National Key Research and Development Plan(No.2017YFB1402103); Xi’an science and technology bureau project (201805037YD15CG21(6)); Beilin science and technology special project No. GX1917.

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Authors and Affiliations

  1. Institute of Computer Science and Engineering, Xi’an University of Technology, Xi’an, China

    Jiulong Zhang & Mingtao Guo

  2. Shaanxi Key Laboratory for Network Computing and Security Technology, Xi’an, China

    Jiulong Zhang

  3. University of North Carolina at Charlotte, Charlotte, NC, USA

    Jianping Fan

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  1. Jiulong Zhang
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  2. Mingtao Guo
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  3. Jianping Fan
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Correspondence to Jiulong Zhang.

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Zhang, J., Guo, M. & Fan, J. A novel generative adversarial net for calligraphic tablet images denoising. Multimed Tools Appl 79, 119–140 (2020). https://doi.org/10.1007/s11042-019-08052-8

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  • DOI: https://doi.org/10.1007/s11042-019-08052-8

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