Abstract
We present a deep residual learning approach to address the single image reflection removal problem. Specifically, residual learning exploits the mapping between the observed image and its comparatively simple reflection information, which is then removed to obtain a clear background. Different from other methods that roughly eliminating the reflections and producing the images with remanent sticking, a novel generative adversarial framework is proposed, where the generator is embedded with the deep residual learning, significantly boosting the performance without impairing the intactness of the background by adversarial training. Moreover, a multi-part balanced loss is introduced with comprehensive consideration on the measure of feature similarity as well as the discriminating ability of GAN. It produces the result of high quality by learning the reflection and the background feature simultaneously. Experiments show that the proposed method achieves a state-of-the-art performance.
Student First Author
The work is supported by the NSFC fund (61332011), Shenzhen Fundamental Research fund (JCYJ20170811155442454, JCYJ20180306172023949), China Postdoctoral Science Foundation (2019TQ0316), and Medical Biometrics Perception and Analysis Engineering Laboratory, Shenzhen, China.
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
References
Levin, A., Weiss, Y.: User assisted separation of reflections from a single image using a sparsity prior. IEEE Trans. Pattern Anal. Mach. Intell. 29(9), 1647–1654 (2007)
Han, B.J., Sim, J.Y.: Reflection removal using low-rank matrix completion. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5438–5446 (2017)
Kong, N., Tai, Y.-W., Shin, S.Y.: A physically-based approach to reflection separation. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9–16. IEEE (2012)
Schechner, Y.Y., Shamir, J., Kiryati, N.: Polarization-based decorrelation of transparent layers: the inclination angle of an invisible surface. In: Proceedings of the Seventh IEEE International Conference on Computer Vision (ICCV), vol. 2, pp. 814–819. IEEE (1999)
Schechner, Y.Y., Kiryati, N., Basri, R.: Separation of transparent layers using focus. Int. J. Comput. Vis. 39(1), 25–39 (2000)
Agrawal, A., Raskar, R., Nayar, S.K., Li, Y.: Removing photography artifacts using gradient projection and flash-exposure sampling. ACM Trans. Graph. (TOG) 24(3), 828–835 (2005)
Li, Y., Brown., M.S.: Single image layer separation using relative smoothness. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2752–2759. IEEE Computer Society (2014)
Arvanitopoulos, N., Achanta, R., Susstrunk, S.: Single image reflection suppression. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4498–4506 (2017)
Fan, Q., Yang, J., Hua, G., Chen, B., Wipf, D.: A generic deep architecture for single image reflection removal and image smoothing. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 3238–3247 (2017)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Huynh-Thu, Q., Ghanbari, M.: Scope of validity of PSNR in image/video quality assessment. Electron. Lett. 44(13), 800–801 (2008)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)
Everingham, M., Eslami, S.A., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes challenge: a retrospective. Int. J. Comput. Vis. 111(1), 98–136 (2015)
Li, J., et al.: A probabilistic hierarchical model for multi-view and multi-feature classification. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)
Li, J., et al.: Generative multi-view and multi-feature learning for classification. Inf. Fusion 45, 215–226 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Xu, Z., Guo, X., Lu, G. (2019). Single Image Reflection Removal Based on Deep Residual Learning. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11858. Springer, Cham. https://doi.org/10.1007/978-3-030-31723-2_22
Download citation
DOI: https://doi.org/10.1007/978-3-030-31723-2_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-31722-5
Online ISBN: 978-3-030-31723-2
eBook Packages: Computer ScienceComputer Science (R0)