Abstract
Image inpainting, also known as image completion, is the process of filling in the missing region of an incomplete image to make the image is visually plausible. Recently, the learning-based inpainting approach has revolutionized image inpainting. A variety of deep learning models have been applied to image inpainting and have achieved amazing results. This study concentrated on the loss function rather than the structure of the model in learning-based approaches. In this paper, a novel loss function containing the combination of edge guided loss term and weighted perceptual loss term was proposed. The proposed method was tested on the test set of ICME 2019 image inpainting challenge, which PSNR score is 1.3354 dB higher than the method without edge guided loss term, SSIM is 0.0014 higher, the qualitative result produced by the proposed method in objects removal task also is visually plausible and pleasing which won the objects removal track in ICME 2019 challenge.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Fehn, C.: Depth-image-based rendering (DIBR), compression, and transmission for a new approach on 3D-TV. Stereoscopic Displays Virtual Reality Syst. XI 5291, 93–105 (2004)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
Iizuka, S., Simo-Serra, E., Ishikawa, H.: Globally and locally consistent image completion. ACM Trans. Graph. 36(4), 1–14 (2017)
Liu, G., Reda, F.A., Shih, K.J., Wang, T.C., Tao, A., Catanzaro, B.: Image inpainting for irregular holes using partial convolutions. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision – ECCV 2018. LNCS, vol. 11215, pp. 89–105. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01252-6_6
Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) Computer Vision – ECCV 2016. LNCS, vol. 9906, pp. 694–711. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_43
Sobel, I.: An Isotropic 3 x 3 Image Gradient Operator (2014)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv Prepr. arXiv:1409.1556 (2014)
Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift, arXiv Prepr. arXiv:1502.03167 (2015)
National Center for Biotechnology Information
Acknowledgements
This research is supported by Sichuan Science and Technology Program (No. 2020YFS0307, No. 2020YFG0430, No. 2019YFS0146, No. 2019YFS0155). Supported by fund No. 2017SCII0215 of Sichuan Civil-Military Integration Institute.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Li, S. et al. (2021). Edge Guided Loss in Image Inpainting. In: Meng, H., Lei, T., Li, M., Li, K., Xiong, N., Wang, L. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2020. Lecture Notes on Data Engineering and Communications Technologies, vol 88. Springer, Cham. https://doi.org/10.1007/978-3-030-70665-4_14
Download citation
DOI: https://doi.org/10.1007/978-3-030-70665-4_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-70664-7
Online ISBN: 978-3-030-70665-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)