Super-resolution image reconstruction based on K-means-Markov network

  • Authors:

  • YanJie Ma;Hua Zhang;Yanbing Xue;Simiao Zhang

  • Affiliations:

  • Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin Univ. of Technology, Tianjin, China and Key Laboratory of Computer Vision and System, Ministry of Education, ...;Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin Univ. of Technology, Tianjin, China and Key Laboratory of Computer Vision and System, Ministry of Education, ...;Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin Univ. of Technology, Tianjin, China and Key Laboratory of Computer Vision and System, Ministry of Education, ...;Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin Univ. of Technology, Tianjin, China and Key Laboratory of Computer Vision and System, Ministry of Education, ...

  • Venue:
  • ICNC'09 Proceedings of the 5th international conference on Natural computation
  • Year:
  • 2009

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Abstract

We address a learning-based method for super resolution. Training sample set provide a candidate highresolution interpretation for the low-resolution images. Modeling image patches as Markov network node, and we learn the parameters of the network from training set, compute probability distribution by K-means algorithm. Given a new low-resolution image to enhance, we select from the training data a set of 10 candidate high-resolution patches for each patch of low-resolution image. In Bayesian belief propagation, we use compatibility relationship between neighboring candidate patches to select the most probable high-resolution candidate. The experimental results show that this method can obtain better result.