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High-order markov random field for single depth image super-resolution

Shabaninia, E ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1049/iet-cvi.2016.0373
  3. Abstract:
  4. Although there is an increasing interest in employing the depth data in computer vision applications, the spatial resolution of depth maps is still limited compared with typical visible-light images. A novel method is proposed to synthetically improve the spatial resolution of a single depth image. It integrates the higher-order terms into the Markov random field (MRF) formulation of example-based methods in order to improve the representational power of those methods. The inference is performed by approximately minimising the higher-order multi-label MRF energies. In addition, to improve the efficiency of the inference algorithm, a hierarchical scheme on the number of MRF states is proposed. First, a large number of states are used to obtain an initial labelling by solving the minimisation problem of inference for only the first-order energies. Then, the problem is solved for the higher-order energies in a smaller number of states. Performance comparisons show that proposed method improves the results of first-order approaches that are based on simple four-connected MRF graph structure, both qualitatively and quantitatively. © The Institution of Engineering and Technology 2017
  5. Keywords:
  6. Image resolution ; Inference engines ; Markov processes ; Computer vision applications ; Example-based methods ; Higher order terms ; Inference algorithm ; Markov Random Fields ; Performance comparison ; Spatial resolution ; Visible light images ; Image enhancement
  7. Source: IET Computer Vision ; Volume 11, Issue 8 , 2017 , Pages 683-690 ; 17519632 (ISSN)
  8. URL: http://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2016.0373