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Image interpolation using Gaussian Mixture Models with spatially constrained patch clustering

Niknejad, M ; Sharif University of Technology | 2015

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  1. Type of Document: Article
  2. DOI: 10.1109/ICASSP.2015.7178243
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2015
  4. Abstract:
  5. In this paper we address the problem of image interpolation using Gaussian Mixture Models (GMM) as a prior. Previous methods of image restoration with GMM have not considered spatial (geometric) distance between patches in clustering, failing to fully exploit the coherency of nearby patches. The GMM framework in our method for image interpolation is based on the assumption that the accumulation of similar patches in a neighborhood are derived from a multivariate Gaussian probability distribution with a specific covariance and mean. An Expectation Maximization-like (EM-like) algorithm is used in order to determine patches in a cluster and restore them. The results show that our image interpolation method outperforms previous state-of-the-art methods with an acceptable bound
  6. Keywords:
  7. Continuation ; Gaussian mixture models ; Image restoration ; Interpolation ; Nighborhood clustering
  8. Source: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 19 April 2014 through 24 April 2014 ; Volume 2015-August , April , 2015 , Pages 1613-1617 ; 15206149 (ISSN) ; 9781467369978 (ISBN)
  9. URL: http://ieeexplore.ieee.org/document/7178243/?reload=true