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Guided Depth Enhancement via Anisotropic Diffusion

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Advances in Multimedia Information Processing – PCM 2013 (PCM 2013)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8294))

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Abstract

In this paper, we propose to conduct inpainting and upsampling for defective depth maps when aligned color images are given. These tasks are referred to as the guided depth enhancement problem. We formulate the problem based on the heat diffusion framework. The pixels with known depth values are treated as the heat sources and the depth enhancement is performed via diffusing the depth from these sources to unknown regions. The diffusion conductivity is designed in terms of the guidance color image so that a linear anisotropic diffusion problem is formed. We further cast the steady state problem of this diffusion into the famous random walk model, by which the enhancement is achieved efficiently by solving a sparse linear system. The proposed algorithm is quantitatively evaluated on the Middlebury stereo dataset and is applied to inpaint Kinect data and upsample Lidar’s range data. Comparisons to the commonly used bilateral filter and Markov Random Field based methods are also presented, showing that our algorithm is competent.

This research work was supported in parts by the National Natural Science Foundation of China via grants 61001171, 61071219, 90820306, and Chinese Universities Scientific Fund.

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Author information

Authors and Affiliations

  1. Dept. of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, 310027, P.R. China

    Junyi Liu & Xiaojin Gong

Authors
  1. Junyi Liu
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  2. Xiaojin Gong
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Editor information

Editors and Affiliations

  1. EURECOM, Multimedia Department, Sophia Antipolis, France

    Benoit Huet

  2. Department of Computer Science, City University of Hong Kong, Tat Chee Ave, Kowloon, Hong Kong

    Chong-Wah Ngo

  3. Nanjing University of Science and Technology, 210093, Nanjing, China

    Jinhui Tang

  4. Department of Computer Science and Technology, Nanjing University, Xianlin Avenue No. 163, 210023, Nanjing, China

    Zhi-Hua Zhou

  5. School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA

    Alexander G. Hauptmann

  6. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117583, Singapore, Singapore

    Shuicheng Yan

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© 2013 Springer International Publishing Switzerland

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Liu, J., Gong, X. (2013). Guided Depth Enhancement via Anisotropic Diffusion. In: Huet, B., Ngo, CW., Tang, J., Zhou, ZH., Hauptmann, A.G., Yan, S. (eds) Advances in Multimedia Information Processing – PCM 2013. PCM 2013. Lecture Notes in Computer Science, vol 8294. Springer, Cham. https://doi.org/10.1007/978-3-319-03731-8_38

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  • DOI: https://doi.org/10.1007/978-3-319-03731-8_38

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03730-1

  • Online ISBN: 978-3-319-03731-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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