Skip to main content

Deep Demosaicing Using ResNet-Bottleneck Architecture

  • 527 Accesses

Part of the Communications in Computer and Information Science book series (CCIS,volume 1148)


Demosaicing is a fundamental step in a camera pipeline to construct a full RGB image from the bayer data captured by a camera sensor. The conventional signal processing algorithms fail to perform well on complex-pattern images giving rise to several artefacts like Moire, color and Zipper artefacts. The proposed deep learning based model removes such artefacts and generates visually superior quality images. The model performs well on both the sRGB (standard RGB color space) and the linear datasets without any need of retraining. It is based on Convolutional Neural Networks (CNNs) and uses a residual architecture with multiple ‘Residual Bottleneck Blocks’ each having 3 CNN layers. The use of 1 \(\times \) 1 kernels allowed to increase the number of filters (width) of the model and hence, learned the inter-channel dependencies in a better way. The proposed network outperforms the state-of-the-art demosaicing methods on both sRGB and linear datasets.


  • Demosaicing
  • RGB
  • Bayer
  • Moire artefacts
  • CNN
  • Residual Bottleneck architecture

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-981-15-4018-9_16
  • Chapter length: 10 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
USD   84.99
Price excludes VAT (USA)
  • ISBN: 978-981-15-4018-9
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   109.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.


  1. Pekkucuksen, I., Altunbasak, Y.: Multiscale gradients-based color filter array interpolation. IEEE Trans. Image Process. 22, 157–165 (2013)

    MathSciNet  CrossRef  Google Scholar 

  2. Monno, Y., Kiku, D., Tanaka, M., Okutomi, M.: Adaptive residual interpolation for color image demosaicking. In: Proceedings of IEEE ICIP 2015, pp. 3861–3865 (2015)

    Google Scholar 

  3. Wang, Y.Q.: A multilayer neural network for image demosaicking. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 1852–1856. IEEE, October 2014

    Google Scholar 

  4. Gharbi, M., Chaurasia, G., Paris, S., Durand, F.: Deep joint demosaicking and denoising. ACM Trans. Graph. (TOG) 35(6), 191 (2016)

    CrossRef  Google Scholar 

  5. Kodak Dataset.

  6. Tan, R., Zhang, K., Zuo, W., Zhang, L.: Color image demosaicking via deep residual learning. In: IEEE International Conference on Multimedia and Expo (ICME) (2017)

    Google Scholar 

  7. Hirakawa, K., Parks, T.W.: Adaptive homogeneity-directed demosaicing algorithm. IEEE Trans. Image Process. 14(3), 360–369 (2005)

    CrossRef  Google Scholar 

  8. Janocha, K., Czarnecki, W.M.: On loss functions for deep neural networks in classification. arXiv preprint arXiv:1702.05659 (2017)

  9. Zhang, L., Wu, X., Buades, A., Li, X.: Color demosaicking by local directional interpolation and nonlocal adaptive thresholding. J. Electron. Imaging 20(2), 023016 (2011)

    CrossRef  Google Scholar 

  10. Syu, N.-S., Chen, Y.-S., Chuang, Y.-Y.: Learning deep convolutional networks for demosaicing. arXiv preprint arXiv:1802.03769 (2018)

  11. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. arXiv preprint arXiv:1512.03385 (2015)

  12. He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 630–645. Springer, Cham (2016).

    CrossRef  Google Scholar 

  13. Syu, N.S., Chen, Y.S., Chuang, Y.Y.: Learning deep convolutional networks for demosaicing. arXiv preprint arXiv:1802.03769 (2018)

  14. Ma, K., et al.: Waterloo exploration database: new challenges for image quality assessment models. IEEE Trans. Image Process. 26(2), 1004–1016 (2016)

    MathSciNet  CrossRef  Google Scholar 

  15. Kokkinos, F., Lefkimmiatis, S.: Deep image demosaicking using a cascade of convolutional residual denoising networks. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 303–319 (2018)

    Google Scholar 

  16. Kokkinos, F., Lefkimmiatis, S.: Iterative joint image demosaicking and denoising using a residual denoising network. IEEE Trans. Image Process. 28, 4177–4188 (2019)

    MathSciNet  CrossRef  Google Scholar 

  17. Khashabi, D., Nowozin, S., Jancsary, J., Fitzgibbon, A.W.: Joint demosaicing and denoising via learned nonparametric random fields. IEEE Trans. Image Process. 23(12), 4968–4981 (2014)

    MathSciNet  CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Divakar Verma .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Verma, D., Kumar, M., Eregala, S. (2020). Deep Demosaicing Using ResNet-Bottleneck Architecture. In: Nain, N., Vipparthi, S., Raman, B. (eds) Computer Vision and Image Processing. CVIP 2019. Communications in Computer and Information Science, vol 1148. Springer, Singapore.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-4017-2

  • Online ISBN: 978-981-15-4018-9

  • eBook Packages: Computer ScienceComputer Science (R0)