Deep Intrinsic Image Decomposition Using Joint Parallel Learning

  • Yuan Yuan
  • Bin ShengEmail author
  • Ping LiEmail author
  • Lei Bi
  • Jinman Kim
  • Enhua Wu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11542)


Intrinsic image decomposition is a highly ill-posed problem in computer vision referring to extract albedo and shading from an image. In this paper, we regard it as an image-to-image translation issue and propose a novel thought, which makes use of parallel convolutional neural networks (ParCNN) to learn albedo and shading with different spatial features and data distributions, respectively. At the same time, the energy is preserved as much as possible under the constraint of image reconstruction loss shared by the two networks. Moreover, we add the gradient prior based on the traditional image formation process into the loss function, which can lead to a performance improvement of our basic learning model by jointing advantages of the physically-based method and the data-driven method. We choose MPI Sintel dataset for model training and testing. Quantitative and qualitative evaluation results outperform the state-of-the-art methods.


Intrinsic image decomposition ParCNN Gradient priors 


  1. 1.
    Barron, J.T., Malik, J.: Shape, illumination, and reflectance from shading. IEEE Trans. Pattern Anal. Mach. Intell. 37(8), 1670–1687 (2015). Scholar
  2. 2.
    Chen, Q., Koltun, V.: A simple model for intrinsic image decomposition with depth cues. In: 2013 IEEE International Conference on Computer Vision, pp. 241–248, December 2013.
  3. 3.
    Grosse, R.B., Johnson, M.K., Adelson, E.H., Freeman, W.T.: Ground truth dataset and baseline evaluations for intrinsic image algorithms. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 2335–2342 (2009)Google Scholar
  4. 4.
    Lettry, L., Vanhoey, K., Gool, L.V.: DARN: a deep adversarial residual network for intrinsic image decomposition. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1359–1367 (2018)Google Scholar
  5. 5.
    Narihira, T., Maire, M., Yu, S.X.: Direct intrinsics: learning albedo-shading decomposition by convolutional regression. In: Computer Science, pp. 2992–2992 (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringShanghai Jiao Tong UniversityShanghaiChina
  2. 2.School of Marine Science and TechnologyNorthwestern Polytechnical UniversityXi’anChina
  3. 3.Faculty of Information TechnologyMacau University of Science and TechnologyMacauChina
  4. 4.Biomedical and Multimedia Information Technology Research Group, School of Information TechnologiesThe University of SydneySydneyAustralia
  5. 5.Faculty of Science and TechnologyUniversity of MacauMacauChina
  6. 6.State Key Laboratory of Computer Science, Institute of SoftwareChinese Academy of SciencesBeijingChina

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