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GMM Based Single Depth Image Super-Resolution

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Computer Vision, Pattern Recognition, Image Processing, and Graphics (NCVPRIPG 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 841))

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Abstract

Super-resolution (SR) is a technique to improve the resolution of an image from a sequence of input images or from a single image. As SR is an ill-posed inverse problem, it leads to many suboptimal solutions. Since modern depth cameras suffer from low-spatial resolution and are noisy, we present a Gaussian mixture model (GMM) based method for depth image super-resolution (SR). We train GMM from a set of high-resolution and low-resolution (HR-LR) synthetic training depth images to learn the relation between the HR and the LR patches in the form of covariance matrices. We use expectation-maximization (EM) algorithm to converge to an optimal solution. We show the promising results qualitatively and quantitatively in comparison to other depth image SR methods.

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Authors and Affiliations

  1. National Institute of Technology Karnataka, Surathkal, Mangalore, India

    Chandra Shaker Balure & M. Ramesh Kini

  2. Indian Institute of Technology Mandi, Mandi, H.P., India

    Arnav Bhavsar

Authors
  1. Chandra Shaker Balure
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  2. M. Ramesh Kini
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  3. Arnav Bhavsar
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Corresponding author

Correspondence to Chandra Shaker Balure .

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Editors and Affiliations

  1. Indian Institute of Technology Mandi, Mandi, Himachal Pradesh, India

    Renu Rameshan

  2. Indraprastha Institute of Information Technology, New Delhi, India

    Chetan Arora

  3. Indian Institute of Technology, New Delhi, India

    Sumantra Dutta Roy

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Balure, C.S., Kini, M.R., Bhavsar, A. (2018). GMM Based Single Depth Image Super-Resolution. In: Rameshan, R., Arora, C., Dutta Roy, S. (eds) Computer Vision, Pattern Recognition, Image Processing, and Graphics. NCVPRIPG 2017. Communications in Computer and Information Science, vol 841. Springer, Singapore. https://doi.org/10.1007/978-981-13-0020-2_22

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  • DOI: https://doi.org/10.1007/978-981-13-0020-2_22

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-0019-6

  • Online ISBN: 978-981-13-0020-2

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