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2019 | OriginalPaper | Chapter

Deep Intrinsic Image Decomposition Using Joint Parallel Learning

Authors : Yuan Yuan, Bin Sheng, Ping Li, Lei Bi, Jinman Kim, Enhua Wu

Published in: Advances in Computer Graphics

Publisher: Springer International Publishing

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Abstract

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.

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Literature
1.
2.
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)
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)
5.
Narihira, T., Maire, M., Yu, S.X.: Direct intrinsics: learning albedo-shading decomposition by convolutional regression. In: Computer Science, pp. 2992–2992 (2015)
Metadata
Title
Deep Intrinsic Image Decomposition Using Joint Parallel Learning
Authors
Yuan Yuan
Bin Sheng
Ping Li
Lei Bi
Jinman Kim
Enhua Wu
Copyright Year
2019
Book
Advances in Computer Graphics

Print ISBN: 978-3-030-22513-1

Electronic ISBN: 978-3-030-22514-8

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-22514-8_28

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