Abstract
The rapid development of deep learning has brought a new development direction for image inpainting, changing the traditional image inpaiting algorithm, which can only repair the problem of small area damage based on the structure and texture of the damaged image. In recent years, image inpainting algorithm based on deep learning has received widespread attention from industry and academia so that it has made great progress. However, the current image inpainting algorithm based on deep learning still has the problem of consuming so much time. In order to solve the above problem, an end-to-end image inpainting algorithm suitable for real-time scene was proposed. The mask of the damaged image generated by D-linkNet network, the edge information of the damaged image, and damaged image were used to control the network input, which avoided the damage to the existing semantics of the image and retained the intact image outside the damaged. On this basis, in order to improve the performance of image inpainting, Convolutional Block Attention Module (CBAM) was used in the residual network. Experimental results show that, compared with edge information-based deep learning algorithm Edge Connect, the repair speed is twice as fast as Edge Connect while the repair results are similar.
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Qin, T., Liu, J., Xue, W. (2021). Research on Efficient Image Inpainting Algorithm Based on Deep Learning. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2021. Lecture Notes in Computer Science(), vol 12736. Springer, Cham. https://doi.org/10.1007/978-3-030-78609-0_10
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DOI: https://doi.org/10.1007/978-3-030-78609-0_10
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