Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Cover of the book

2018 | OriginalPaper | Chapter

Deep Boosting for Image Denoising

Authors : Chang Chen, Zhiwei Xiong, Xinmei Tian, Feng Wu

Published in: Computer Vision – ECCV 2018

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Boosting is a classic algorithm which has been successfully applied to diverse computer vision tasks. In the scenario of image denoising, however, the existing boosting algorithms are surpassed by the emerging learning-based models. In this paper, we propose a novel deep boosting framework (DBF) for denoising, which integrates several convolutional networks in a feed-forward fashion. Along with the integrated networks, however, the depth of the boosting framework is substantially increased, which brings difficulty to training. To solve this problem, we introduce the concept of dense connection that overcomes the vanishing of gradients during training. Furthermore, we propose a path-widening fusion scheme cooperated with the dilated convolution to derive a lightweight yet efficient convolutional network as the boosting unit, named Dilated Dense Fusion Network (DDFN). Comprehensive experiments demonstrate that our DBF outperforms existing methods on widely used benchmarks, in terms of different denoising tasks.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
next chapter Self-Supervised Relative Depth Learning for Urban Scene Understanding
Literature
1.
Zoran, D., Weiss, Y.: From learning models of natural image patches to whole image restoration. In: IEEE International Conference on Computer Vision (ICCV), pp. 479–486 (2011)
2.
Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 60–65, June 2005
3.
Buades, A., Coll, B., Morel, J.: Nonlocal image and movie denoising. Int. J. Comput. Vis. (IJCV) 76(2), 123–139 (2008)CrossRef
4.
Mairal, J., Bach, F., Ponce, J., Sapiro, G., Zisserman, A.: Non-local sparse models for image restoration. In: IEEE International Conference on Computer Vision (ICCV), pp. 2272–2279 (2009)
5.
Elad, M., Aharon, M.: Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Process. 15(12), 3736–3745 (2006)MathSciNetCrossRef
6.
Dong, W., Zhang, L., Shi, G., Li, X.: Nonlocally centralized sparse representation for image restoration. IEEE Trans. Image Process. 22(4), 1620–1630 (2013)MathSciNetCrossRef
7.
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
8.
Schmidt, U., Roth, S.: Shrinkage fields for effective image restoration. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2774–2781, June 2014
9.
Gu, S., Zhang, L., Zuo, W., Feng, X.: Weighted nuclear norm minimization with application to image denoising. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2862–2869, June 2014
10.
Burger, H.C., Schuler, C.J., Harmeling, S.: Image denoising: can plain neural networks compete with BM3D? In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2392–2399, June 2012
11.
Chen, Y., Pock, T.: Trainable nonlinear reaction diffusion: a flexible framework for fast and effective image restoration. IEEE Trans. Patt. Anal. Mach. Intell. 39(6), 1256 (2017)CrossRef
12.
Zhang, K., Zuo, W., Chen, Y., Meng, D., Zhang, L.: Beyond a gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans. Image Process. 26(7), 3142–3155 (2017)MathSciNetCrossRef
13.
Remez, T., Litany, O., Giryes, R., Bronstein, A.M.: Deep class-aware image denoising. In: International Conference on Sampling Theory and Applications (SampTA), pp. 138–142, July 2017
14.
Charest, M.R., Elad, M., Milanfar, P.: A general iterative regularization framework for image denoising. In: Conference on Information Sciences and Systems, pp. 452–457, March 2006
15.
Milanfar, P.: A tour of modern image filtering: new insights and methods, both practical and theoretical. IEEE Sig. Process. Mag. 30(1), 106–128 (2013)MathSciNetCrossRef
16.
Talebi, H., Zhu, X., Milanfar, P.: How to SAIF-ly boost denoising performance. IEEE Trans. Image Process. 22(4), 1470–1485 (2013)MathSciNetCrossRef
17.
18.
Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2261–2269, July 2017
19.
Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. In: International Conference on Learning Representations (ICLR) (2016)
20.
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning (ICML), pp. 448–456 (2015)
21.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778, June 2016
22.
Buades, A., Coll, B., Morel, J.M.: A review of image denoising algorithms, with a new one. SIAM J. Multiscale Model. Simul. 4(2), 490–530 (2005)MathSciNetCrossRef
23.
Han, S., Meng, Z., Khan, A.S., Tong, Y.: Incremental boosting convolutional neural network for facial action unit recognition. In: Lee, D.D., Sugiyama, M., Luxburg, U.V., Guyon, I., Garnett, R. (eds.) Advances in Neural Information Processing Systems (NIPS), pp. 109–117. Curran Associates, Inc. (2016)
24.
Moghimi, M., Saberian, M., Yang, J., Li, L.J., Vasconcelos, N., Belongie, S.: Boosted convolutional neural networks. In: British Machine Vision Conference (BMVC), pp. 24.1–24.13 (2016)
25.
Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: International Conference on Machine Learning (ICML), pp. 807–814 (2010)
26.
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826, June 2016
27.
Kim, J., Lee, J.K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1646–1654, June 2016
28.
Yu, F., Koltun, V., Funkhouser, T.: Dilated residual networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 636–644, July 2017
29.
Roth, S., Black, M.J.: Fields of experts: a framework for learning image priors. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 860–867, June 2005
30.
Dong, C., Deng, Y., Loy, C.C., Tang, X.: Compression artifacts reduction by a deep convolutional network. In: IEEE International Conference on Computer Vision (ICCV), pp. 576–584, December 2015
31.
Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (ICLR), vol. 5 (2015)
32.
He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In: IEEE International Conference on Computer Vision (ICCV), pp. 1026–1034, December 2015
33.
Vemulapalli, R., Tuzel, O., Liu, M.Y.: Deep Gaussian conditional random field network: a model-based deep network for discriminative denoising. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4801–4809, June 2016
34.
Lefkimmiatis, S.: Non-local color image denoising with convolutional neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5882–5891, July 2017
35.
Kim, Y., Jung, H., Min, D., Sohn, K.: Deeply aggregated alternating minimization for image restoration. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 284–292, July 2017
36.
Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Color image denoising via sparse 3D collaborative filtering with grouping constraint in luminance-chrominance space. In: IEEE International Conference on Image Processing (ICIP), vol. 1, I-313–I-316, September 2007
37.
Shi, Z., Chen, C., Xiong, Z., Liu, D., Wu, F.: HSCNN+: advanced CNN-based hyperspectral recovery from RGB images. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 2018
Metadata
Title
Deep Boosting for Image Denoising
Authors
Chang Chen
Zhiwei Xiong
Xinmei Tian
Feng Wu
Copyright Year
2018
Book
Computer Vision – ECCV 2018

Print ISBN: 978-3-030-01251-9

Electronic ISBN: 978-3-030-01252-6

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-01252-6_1

Premium Partner

    Image Credits