Limited recurrent neural network for superresolution image reconstruction

  • Authors:

  • Yan Zhang;Qing Xu;Tao Wang;Lei Sun

  • Affiliations:

  • Zhengzhou Institute of Surveying and Mapping, Zhengzhou, China;Zhengzhou Institute of Surveying and Mapping, Zhengzhou, China;Zhengzhou Institute of Surveying and Mapping, Zhengzhou, China;Zhengzhou Institute of Surveying and Mapping, Zhengzhou, China

  • Venue:
  • ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
  • Year:
  • 2006

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Abstract

The paper proposes a new method for image resolution enhancement from multiple images using the limited recurrent neural network (LRNN) approach, which is a set of collectively operating feed-forward neural networks. In the limited recurrent networks, information about past outputs is fed back through recurrent connections of output units and mixed with the input nodes flowing into the network input as external input nodes. Thus, experience about past search is utilized, which enables LRNN to be capable of both learning and searching the optimal solution for optimization problems in the solution space. Estimates computed from a low-resolution (LR) simulation image sequence and an actual video film sequence show dramatic visual and quantitative improvements over bilinear interpolation, and equivalent performance to that of the frequency domain approach.