A Generic Deep Architecture for Single Image Reflection Removal and Image Smoothing

Proceedings of the IEEE International Conference on Computer Vision

Volumn 2017-October, Issue , 2017, Pages 3258-3267

  Fan, Qingnan ^a   Yang, Jiaolong ^b   Hua, Gang ^b   Chen, Baoquan ^a   Wipf, David ^b  

^a SHANDONG UNIVERSITY   (China)

^b MICROSOFT RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; DEEP LEARNING; DEEP NEURAL NETWORKS; INFORMATION FILTERING;

DEEP ARCHITECTURES; EDGE INFORMATION; LEARNING STRATEGY; LOW-LEVEL VISION; NEURAL NETWORK STRUCTURES; REFLECTION REMOVALS; STATE OF THE ART; SYNTHETIC DATA GENERATIONS;

IMAGE PROCESSING;

EID: 85041930753     PISSN: 15505499     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2017.351     Document Type: Conference Paper

References (43)

1. A. Agrawal, R. Raskar, S. K. Nayar, and Y. Li. Removing photography artifacts using gradient projection and flashexposure sampling. ACM Transactions on Graphics (TOG), 24 (3):828-835, 2005.
2. S. Bi, X. Han, and Y. Yu. An L1 image transform for edgepreserving smoothing and scene-level intrinsic decomposition. ACM Transactions on Graphics (TOG), 34 (4):78, 2015.
3. J. Canny. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), (6):679-698, 1986.
4. M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman. The pascal visual object classes (voc) challenge. International Journal of Computer Vision (IJCV), 88 (2):303-338, 2010.
5. Q. Fan, D. Wipf, G. Hua, and B. Chen. Revisiting deep image smoothing and intrinsic image decomposition. arXiv preprint. arXiv:1701. 02965, 2017.
6. Z. Farbman, R. Fattal, D. Lischinski, and R. Szeliski. Edgepreserving decompositions for multi-scale tone and detail manipulation. ACM Transactions on Graphics (TOG), 27 (3), 2008.
7. H. Farid and E. H. Adelson. Separating reflections and lighting using independent components analysis. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), volume 1, pages 262-267, 1999.
8. R. Fattal, M. Agrawala, and S. Rusinkiewicz. Multiscale shape and detail enhancement from multi-light image collections. ACM Transactions on Graphics (TOG), 26 (3), 2007.
9. K. Gai, Z. Shi, and C. Zhang. Blind separation of superimposed moving images using image statistics. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 34 (1):19-32, 2012.
10. E. S. Gastal and M. M. Oliveira. Domain transform for edgeaware image and video processing. In ACM Transactions on Graphics (TOG), volume 30, page 69. ACM, 2011.
11. M. Gharbi, G. Chaurasia, S. Paris, and F. Durand. Deep joint demosaicking and denoising. ACM Transactions on Graphics (TOG), 35 (6):191, 2016.
12. X. Guo, X. Cao, and Y. Ma. Robust separation of reflection from multiple images. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2187-2194, 2014.
13. K. He, X. Zhang, S. Ren, and J. Sun. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In IEEE International Conference on Computer Vision (ICCV), pages 1026-1034, 2015.
14. K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770-778, 2016.
15. S. H. Khan, M. Bennamoun, F. Sohel, and R. Togneri. Automatic shadow detection and removal from a single image. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 38 (3):431-446, 2016.
16. J. Kim, J. Kwon Lee, and K. Mu Lee. Accurate image super-resolution using very deep convolutional networks. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1646-1654, 2016.
17. J. Kim, J. Kwon Lee, and K. Mu Lee. Deeply-recursive convolutional network for image super-resolution. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1637-1645, 2016.
18. D. Kingma and J. Ba. Adam: A method for stochastic optimization. International Conference on Learning Representations (ICLR), 2015.
19. N. Kong, Y.-W. Tai, and J. S. Shin. A physically-based approach to reflection separation: from physical modeling to constrained optimization. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 36 (2):209-221, 2014.
20. A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems (NIPS), pages 1097-1105, 2012.
21. A. Levin and Y. Weiss. User assisted separation of reflections from a single image using a sparsity prior. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 29 (9), 2007.
22. Y. Li and M. S. Brown. Exploiting reflection change for automatic reflection removal. In IEEE International Conference on Computer Vision (ICCV), pages 2432-2439, 2013.
23. Y. Li and M. S. Brown. Single image layer separation using relative smoothness. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2752-2759, 2014.
24. S. Liu, J. Pan, and M.-H. Yang. Learning recursive filters for low-level vision via a hybrid neural network. In European Conference on Computer Vision (ECCV), 2016.
25. T. Narihira, M. Maire, and S. X. Yu. Direct intrinsics: Learning albedo-shading decomposition by convolutional regression. In IEEE International Conference on Computer Vision (CVPR), pages 2992-2992, 2015.
26. S. Paris and F. Durand. A fast approximation of the bilateral filter using a signal processing approach. In European Conference on Computer Vision (ECCV), pages 568-580, 2006.
27. J. S. Ren and L. Xu. On vectorization of deep convolutional neural networks for vision tasks. AAAI Conference on Artificial Intelligence, 2015.
28. O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei. ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision (IJCV), 115 (3):211-252, 2015.
29. B. Sarel and M. Irani. Separating transparent layers through layer information exchange. In European Conference on Computer Vision (ECCV), pages 328-341, 2004.
30. Y. Y. Schechner, N. Kiryati, and R. Basri. Separation of transparent layers using focus. International Journal of Computer Vision (IJCV), 39 (1):25-39, 2000.
31. Y. Y. Schechner, J. Shamir, and N. Kiryati. Polarization and statistical analysis of scenes containing a semireflector. JOSA A, 17 (2):276-284, 2000.
32. Y. Shih, D. Krishnan, F. Durand, and W. T. Freeman. Reflection removal using ghosting cues. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 3193-3201, 2015.
33. S. N. Sinha, J. Kopf, M. Goesele, D. Scharstein, and R. Szeliski. Image-based rendering for scenes with reflections. ACM Transactions on Graphics (TOG), 31 (4):100-1, 2012.
34. R. Szeliski, S. Avidan, and P. Anandan. Layer extraction from multiple images containing reflections and transparency. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), volume 1, pages 246-253, 2000.
35. R. Wan, B. Shi, T. A. Hwee, and A. C. Kot. Depth of field guided reflection removal. In Image Processing (ICIP), 2016 IEEE International Conference on, pages 21-25. IEEE, 2016.
36. S. Xie and Z. Tu. Holistically-nested edge detection. In International Conference on Computer Vision (ICCV), pages 1395-1403, 2015.
37. L. Xu, C. Lu, Y. Xu, and J. Jia. Image smoothing via L0 gradient minimization. In ACM Transactions on Graphics (TOG), volume 30, page 174, 2011.
38. L. Xu, J. S. Ren, Q. Yan, R. Liao, and J. Jia. Deep edge-aware filters. In International Conference on Machine Learning (ICML), pages 1669-1678, 2015.
39. L. Xu, Q. Yan, Y. Xia, and J. Jia. Structure extraction from texture via natural variation measure. ACM Transactions on Graphics (TOG), 2012.
40. T. Xue, M. Rubinstein, C. Liu, and W. T. Freeman. A computational approach for obstruction-free photography. ACM Transactions on Graphics (TOG), 34 (4):79, 2015.
41. J. Yang, H. Li, Y. Dai, and R. T. Tan. Robust optical flow estimation of double-layer images under transparency or reflection. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1410-1419, 2016.
42. Q. Zhang, X. Shen, L. Xu, and J. Jia. Rolling guidance filter. In European Conference on Computer Vision (ECCV), pages 815-830, 2014.
43. Q. Zhang, L. Xu, and J. Jia. 100+ times faster weighted median filter. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014.