Abstract
Reflection in an image is not always desirable due to the loss of information. Conventional methods to remove reflection are based on priors that require certain conditions to be fulfilled. Recent advancements of deep learning in many fields have revolutionized these traditional approaches. Using input images more than one reduces the ill-posedness of the problem statement. Standard assumption in numerous methods assumes background is stationary and only reflection layer is varying. However, images at different angles have slightly different backgrounds. Considering this a new dataset is created where both reflection and background layer is varying. In this paper, a two-image based method with an end to end mapping between the observed images and background is presented. The key feature is the practicability of the method, wherein a sequence of images at slightly different angles can easily be captured using modern dual camera mobile devices. A combination of feature loss with MSE maintains the content and quality of the resultant image.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
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). https://doi.org/10.1109/TPAMI.2007.1106
Li, Y., Brown, M-S.: Single image layer separation using relative smoothness. In: IEEE Conference on Computer Vision and Pattern Recognition, Columbus, pp. 2752–2759 (2014). https://doi.org/10.1109/CVPR.2014.346
Shih, Y., Krishnan, D., Durand, F., Freeman, W.: Reflection removal using ghosting cues. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, pp. 3193–3201 (2015). https://doi.org/10.1109/CVPR.2015.7298939
Tao, M.W., Hadap, S., Malik, J., Ramamoorthi, R.: Depth from combining defocus and correspondence using light-field cameras. In: 2013 IEEE International Conference on Computer Vision, Sydney, pp. 673–680 (2013). https://doi.org/10.1109/ICCV.2013.89
Wan, R., Shi, B., Tan, A.H., Kot, A.C.: Depth of field guided reflection removal. In: Paper presented at the meeting of the ICIP), Phoenix, AZ, pp. 21–25 (2016). https://doi.org/10.1109/ICIP.2016.7532311
Fergus, R., Singh, B., Hertzmann, A., Roweis, S., Freeman, W.: Removing camera shake from a single photograph. ACM Trans. Graph. 25, 787–794 (2006). https://doi.org/10.1145/1179352.1141956
Fan, Q., Yang, J., Hua, G., Chen, B., Wipf, D.: A generic deep architecture for single image reflection removal and image smoothing. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice. Italy (2017). https://doi.org/10.1109/ICCV.2017.351
Kong, N., Tai, Y.-W., Shin, J.S.: A physically-based approach to reflection separation: from physical modeling to constrained optimization. IEEE Trans. Pattern Anal. Mach. Intell. 36(2), 209–221 (2014). https://doi.org/10.1109/TPAMI.2013.45
Wolff, L-B.: Using polarization to separate reflection components. In: Proceedings CVPR 1989: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA¸pp. 363–369 (1989). https://doi.org/10.1109/CVPR.1989.37873
Li, Y., Brown, S-M.: Exploiting reflection change for automatic reflection removal. In: IEEE International Conference on Computer Vision, Sydney, NSW, pp. 2432–2439 (2013). https://doi.org/10.1109/ICCV.2013.302
Gai, K., Shi, Z., Zhang, C.: Blind Separation of superimposed moving images using image statistics. IEEE Trans. Pattern Anal. Mach. Intell. 34(1), 19–32 (2012). https://doi.org/10.1109/TPAMI.2011.87
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, Greece, vol. 2, pp. 814–819 (1999). https://doi.org/10.1109/ICCV.1999.790305
Chi, Z., Wu, X., Shu, X., Gu, J.: Single Image Reflection Removal Using Deep Encoder-Decoder Network. CoRR (2018). arXiv preprint arXiv:1802.00094v1
Lee, D., Yang, M-H., Oh, S.: Generative Single Image Reflection Separation (2018). arXiv preprint arXiv:1801.04102
Kuanar, S., Rao, K., Mahapatra, D., Bilas, M.: Night Time Haze and Glow Removal using Deep Dilated Convolutional Network (2019). arXiv preprint arXiv:1902.00855v1
Yang, J., Gong, D., Liu, L., Shi, Q.: Seeing deeply and bidirectionally: a deep learning approach for single image reflection removal. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11207, pp. 675–691. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01219-9_40
Jin, M., Süsstrunk, S., Favaro, P.: Learning to see through reflections. In: IEEE International Conference on Computational Photography (ICCP), Pittsburgh, PA, p. 12 (2018). https://doi.org/10.1109/ICCPHOT.2018.8368464
Wan, R., Shi, B., Duan, L.Y., Tan, A.-H., Kot, A.C.: CRRN: multi-scale guided concurrent reflection removal network. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA, pp. 4777–4785 (2018). https://doi.org/10.1109/CVPR.2018.00502
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.) ECCV 2016. LNCS, vol. 9906, pp. 694–711. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_43
Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48
Zhang, X., Ng, R., Chen, Q.: Single image reflection separation with perceptual losses. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, pp. 4786–4794 (2018). https://doi.org/10.1109/CVPR.2018.00503
Dumoulin, V., Visin, F.: A guide to convolution arithmetic for deep learning (2016). arXiv preprint arXiv:1603.07285v2
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd International Conference on International Conference on Machine Learning, Lille, France, vol. 37, pp. 448–456 (2015). arXiv preprint arXiv:1502.03167v3
Maas, A.L., Hannun, A.-Y., Ng, A.-Y.: Rectifier nonlinearities improve neural network acoustic models. In: Proceedings of the International Conference on Machine Learning (ICML), vol. 30, no. 1, p. 3 (2013). arXiv preprint arXiv:1804.02763v1
Chen, Q., Koltun, V.: Photographic image synthesis with cascaded refinement networks. In: IEEE International Conference on Computer Vision (ICCV), Venice, pp. 1520–1529 (2017). https://doi.org/10.1109/ICCV.2017.168
Ledig, C., et al.: In photo-realistic single image super-resolution using a generative adversarial network. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, pp. 105–114 (2017). https://doi.org/10.1109/CVPR.2017.19
Gatys, L.-A., Ecker, A.-S., Bethge, M.: Texture synthesis using convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 1, pp. 262–270 (2015). https://doi.org/10.5555/2969239.2969269
Gatys, L.-A., Ecker, A.-S., Bethge, M.: A neural algorithm of artistic style (2015). arXiv preprint arXiv:1508.06576
Chang, Y., Jung, C.: Single image reflection removal using convolutional neural networks. IEEE Trans. Image Process. 28(4), 1954–1966 (2019). https://doi.org/10.1109/TIP.2018.2880088
Arvanitopoulos, N., Achanta, R., Süsstrunk, S.: Single image reflection suppression. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, pp. 1752–1760 (2017). https://doi.org/10.1109/CVPR.2017.190
Wei, K., Yang, J., Fu, Y., Wipf, D., Huang, H.: Single image reflection removal exploiting misaligned training data and network enhancements. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, pp. 8170–8179 (2019). https://doi.org/10.1109/CVPR.2019.00837
Li, C., Yang, Y., He, K., Lin, S., Hopcroft, J.: Single Image Reflection Removal through Cascaded Refinement (2019). arXiv preprint arXiv:1911.06634v2
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Chaurasiya, R., Ganotra, D. (2021). Two-Image Approach to Reflection Removal with Deep Learning. In: Singh, S.K., Roy, P., Raman, B., Nagabhushan, P. (eds) Computer Vision and Image Processing. CVIP 2020. Communications in Computer and Information Science, vol 1377. Springer, Singapore. https://doi.org/10.1007/978-981-16-1092-9_6
Download citation
DOI: https://doi.org/10.1007/978-981-16-1092-9_6
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-1091-2
Online ISBN: 978-981-16-1092-9
eBook Packages: Computer ScienceComputer Science (R0)