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Erschienen in:
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2018 | OriginalPaper | Buchkapitel

Decouple Learning for Parameterized Image Operators

verfasst von : Qingnan Fan, Dongdong Chen, Lu Yuan, Gang Hua, Nenghai Yu, Baoquan Chen

Erschienen in: Computer Vision – ECCV 2018

Verlag: Springer International Publishing

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Abstract

Many different deep networks have been used to approximate, accelerate or improve traditional image operators, such as image smoothing, super-resolution and denoising. Among these traditional operators, many contain parameters which need to be tweaked to obtain the satisfactory results, which we refer to as “parameterized image operators”. However, most existing deep networks trained for these operators are only designed for one specific parameter configuration, which does not meet the needs of real scenarios that usually require flexible parameters settings. To overcome this limitation, we propose a new decouple learning algorithm to learn from the operator parameters to dynamically adjust the weights of a deep network for image operators, denoted as the base network. The learned algorithm is formed as another network, namely the weight learning network, which can be end-to-end jointly trained with the base network. Experiments demonstrate that the proposed framework can be successfully applied to many traditional parameterized image operators. We provide more analysis to better understand the proposed framework, which may inspire more promising research in this direction. Our codes and models have been released in https://​github.​com/​fqnchina/​DecoupleLearning​.

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Literatur
1.
Andrychowicz, M., et al.: Learning to learn by gradient descent by gradient descent. In: Advances in Neural Information Processing Systems, pp. 3981–3989 (2016)
2.
Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 2, pp. 60–65. IEEE (2005)
3.
Chen, D., Liao, J., Yuan, L., Yu, N., Hua, G.: Coherent online video style transfer. In: Proceedings of the International Conference on Computer Vision (ICCV) (2017)
4.
Chen, D., Yuan, L., Liao, J., Yu, N., Hua, G.: Stylebank: an explicit representation for neural image style transfer. In: Proceedings of the CVPR, vol. 1, p. 4 (2017)
5.
Chen, D., Yuan, L., Liao, J., Yu, N., Hua, G.: Stereoscopic neural style transfer. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 10 (2018)
6.
Chen, Q., Xu, J., Koltun, V.: Fast image processing with fully-convolutional networks. In: IEEE International Conference on Computer Vision, vol. 9 (2017)
7.
Chen, Y., Hoffman, M.W., Colmenarejo, S.G., Denil, M., Lillicrap, T.P., de Freitas, N.: Learning to learn for global optimization of black box functions. In: International Conference on Machine Learning (2017)
8.
Cheng, B., et al.: Robust emotion recognition from low quality and low bit rate video: a deep learning approach. In: 2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII), pp. 65–70. IEEE (2017)
9.
Cheng, B., Wei, Y., Shi, H., Feris, R., Xiong, J., Huang, T.: Revisiting RCNN: on awakening the classification power of faster RCNN. In: ECCV (2018)CrossRef
10.
Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans. Image Process. 16(8), 2080–2095 (2007)MathSciNetCrossRef
11.
Dai, X., Ng, J.Y.H., Davis, L.S.: FASON: first and second order information fusion network for texture recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 7352–7360 (2017)
12.
Dai, X., Singh, B., Zhang, G., Davis, L.S., Qiu Chen, Y.: Temporal context network for activity localization in videos. In: The IEEE International Conference on Computer Vision (ICCV) (2017)
13.
Dong, C., Deng, Y., Change Loy, C., Tang, X.: Compression artifacts reduction by a deep convolutional network. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 576–584 (2015)
14.
15.
Elad, M., Aharon, M.: Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Process. 15(12), 3736–3745 (2006)MathSciNetCrossRef
16.
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 16th International Conference on Computer Vision (ICCV), pp. 3238–3247 (2017)
17.
Fan, Q., Yang, J., Hua, G., Chen, B., Wipf, D.: Revisiting deep intrinsic image decompositions (2018)
18.
Farbman, Z., Fattal, R., Lischinski, D., Szeliski, R.: Edge-preserving decompositions for multi-scale tone and detail manipulation. In: ACM Transactions on Graphics (TOG), vol. 27, p. 67. ACM (2008)CrossRef
19.
Fu, X., Huang, J., Ding, X., Liao, Y., Paisley, J.: Clearing the skies: a deep network architecture for single-image rain removal. IEEE Trans. Image Process. 26(6), 2944–2956 (2017)MathSciNetCrossRef
20.
Fu, X., Huang, J., Zeng, D., Huang, Y., Ding, X., Paisley, J.: Removing rain from single images via a deep detail network. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1715–1723 (2017)
21.
Gu, S., Zhang, L., Zuo, W., Feng, X.: Weighted nuclear norm minimization with application to image denoising. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2862–2869 (2014)
22.
Ha, D., Dai, A., Le, Q.V.: Hypernetworks. In: ICLR (2018)
23.
He, M., Chen, D., Liao, J., Sander, P.V., Yuan, L.: Deep exemplar-based colorization. ACM Trans. Graph. 37, 47 (2018). Proceedings of SIGGRAPH 2018
24.
Karacan, L., Erdem, E., Erdem, A.: Structure-preserving image smoothing via region covariances. ACM Trans. Graph. (TOG) 32(6), 176 (2013)CrossRef
25.
Kim, J., Kwon Lee, J., Mu Lee, K.: Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1646–1654 (2016)
26.
Kligvasser, I., Shaham, T.R., Michaeli, T.: xUnit: learning a spatial activation function for efficient image restoration. In: CVPR (2018)
27.
Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: CVPR, vol. 2, p. 4 (2017)
28.
Li, D., He, X., Huang, Q., Sun, M.T., Zhang, L.: Generating diverse and accurate visual captions by comparative adversarial learning. arXiv preprint arXiv:​1804.​00861 (2018)
29.
Li, Y., Dixit, M., Vasconcelos, N.: Deep scene image classification with the MFAFVNet. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5746–5754 (2017)
30.
Lin, K., Li, D., He, X., Zhang, Z., Sun, M.T.: Adversarial ranking for language generation. In: Advances in Neural Information Processing Systems, pp. 3155–3165 (2017)
31.
32.
Ma, S., Fu, J., Chen, C.W., Mei, T.: DA-GAN: instance-level image translation by deep attention generative adversarial networks
33.
Mao, X., Shen, C., Yang, Y.B.: Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections. In: Advances in Neural Information Processing Systems, pp. 2802–2810 (2016)
34.
Osher, S., Rudin, L.I.: Feature-oriented image enhancement using shock filters. SIAM J. Numer. Anal. 27(4), 919–940 (1990)CrossRef
35.
Qi, G.J., Zhang, L., Hu, H., Edraki, M., Wang, J., Hua, X.S.: Global versus localized generative adversarial nets. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)
36.
Schmidhuber, J.: Learning to control fast-weight memories: an alternative to dynamic recurrent networks. Neural Comput. 4(1), 131–139 (1992)CrossRef
37.
Sun, J., Xu, Z., Shum, H.Y.: Image super-resolution using gradient profile prior. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8. IEEE (2008)
38.
Tai, Y., Yang, J., Liu, X., Xu, C.: MemNet: A persistent memory network for image restoration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4539–4547 (2017)
39.
Tipping, M.E., Bishop, C.M.: Bayesian image super-resolution. In: Advances in Neural Information Processing Systems, pp. 1303–1310 (2003)
40.
Ulyanov, D., Vedaldi, A., Lempitsky, V.: Improved texture networks: maximizing quality and diversity in feed-forward stylization and texture synthesis. In: Proceedings of the CVPR (2017)
41.
Wichrowska, O., et al.: Learned optimizers that scale and generalize. In: International Conference on Machine Learning (2017)
42.
Xu, L., Lu, C., Xu, Y., Jia, J.: Image smoothing via L 0 gradient minimization. In: ACM Transactions on Graphics (TOG), vol. 30, p. 174. ACM (2011)
43.
Xu, L., Lu, C., Xu, Y., Jia, J.: Image smoothing via l0 gradient minimization. ACM Trans. Graph. 30, 174 (2011). SIGGRAPH Asia
44.
Xu, L., Ren, J., Yan, Q., Liao, R., Jia, J.: Deep edge-aware filters. In: International Conference on Machine Learning, pp. 1669–1678 (2015)
45.
Xu, L., Yan, Q., Xia, Y., Jia, J.: Structure extraction from texture via relative total variation. ACM Trans. Graph. (TOG) 31(6), 139 (2012)
46.
Yang, J., Wright, J., Huang, T.S., Ma, Y.: Image super-resolution via sparse representation. IEEE Trans. Image Process. 19(11), 2861–2873 (2010)MathSciNetCrossRef
47.
Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. In: ICLR (2016)
48.
Zhang, D., Dai, X., Wang, X., Wang, Y.F.: S3D: Single shot multi-span detector via fully 3D convolutional network. In: British Machine Vision Conference (BMVC) (2018)
49.
Zhang, H., Patel, V.M.: Density-aware single image de-raining using a multi-stream dense network. In: CVPR (2018)
50.
51.
Zhang, Q., Xu, L., Jia, J.: 100+ times faster weighted median filter (WMF). In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2830–2837 (2014)
52.
Zoran, D., Weiss, Y.: From learning models of natural image patches to whole image restoration. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 479–486. IEEE (2011)
Metadaten
Titel
Decouple Learning for Parameterized Image Operators
verfasst von
Qingnan Fan
Dongdong Chen
Lu Yuan
Gang Hua
Nenghai Yu
Baoquan Chen
Copyright-Jahr
2018
Buch
Computer Vision – ECCV 2018

Print ISBN: 978-3-030-01260-1

Electronic ISBN: 978-3-030-01261-8

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-030-01261-8_27