Abstract
In recent years, deep learning has shown significant advantages in image restoration. Compared with the traditional repair method, the image repair method based on deep learning can better solve the problem of image missing blur, but it will also cause the problem of local color difference of the repair image. In this paper, an image repair model based on deep residual network is proposed, which is divided into repair module and mitigation module. The repair module uses part of the convolution network to repair the missing area of image blur, the mitigation module uses the deep residual network to adjust the color difference of the repaired image, and the two modules coordinate with each other to make the image repair effect closer to the real image. The experimental results show that the method proposed in this paper has better effect on image repair.
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References
Zifeng, W., Shen, C., van den Hengel, A.: Wider or deeper: revisiting the ResNet model for visual recognition. Pattern Recogn. 90, 119–133 (2019). https://doi.org/10.1016/j.patcog.2019.01.006
He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Conference on Computer Vision and Pattern Recognition, pp. 770–778. IEEE, USA (2016)
Zheng, C., Cham, T., Cai, J.: Pluralistic image completion. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1438–1447. IEEE, USA (2019)
Yu, J., Lin, Z., Yang, J., et al.: Generative image in painting with contextual attention. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 5505–5514. IEEE, USA (2018)
Liu, G., Reda, A., Shih, K., et al.: Image inpainting for irregular Holes using partial convolutions. ACM Trans. Graph. 9(3), 37–51 (2018)
Harley, A., Derpanis, K., Kokkinos, I., et al.: Segmentation-aware convolutional networks using local attention masks. In: IEEE International Conference on Computer Vision, pp. 22–29. IEEE, Italy (2017)
Ledig, C., Theis, L., Huszar, F., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: Conference on Computer Vision and Pattern Recognition, pp. 105–114. IEEE, USA (2017)
Zeng, Y., Fu, J., Chao, H., et al.: Learning pyramid-context encoder network for high-quality image inpainting. In: Conference on Computer Vision and Pattern Recognition, pp. 486–1494. IEEE, USA (2019)
Zhou, B., Lapedriza, A., Khosla, A., et al.: Places: a 10 million image database for scene recognition. IEEE Trans. Pattern Anal. Mach. Intell. 40(6), 1452–1464 (2018)
Acknowledgment
This research was supported by Scientific Research Fund of Hunan Provincial Education Department (18A332), the Science and Technology Plan Project of Hunan Province (2016TP1020), the Application-Oriented Special Disciplines, Double First-Class University Project of Hunan Province (Xiangjiaotong [2018] 469), Internet of things, a first-class undergraduate major in Hunan Province (Xiangjiaotong [2020] 248, No: 288), National College Students’ innovation and entrepreneurship training program (s202010546008), and Innovation and entrepreneurship training program for college students in Hunan Province (20203227).
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Deng, H., Lin, Z., Li, J., Yao, M., Wang, T., Luo, H. (2022). Image Repair Methods Based on Deep Residual Networks. In: Liu, Q., Liu, X., Chen, B., Zhang, Y., Peng, J. (eds) Proceedings of the 11th International Conference on Computer Engineering and Networks. Lecture Notes in Electrical Engineering, vol 808. Springer, Singapore. https://doi.org/10.1007/978-981-16-6554-7_18
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DOI: https://doi.org/10.1007/978-981-16-6554-7_18
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