Journal of Systems Engineering and Electronics ›› 2010, Vol. 21 ›› Issue (6): 934-940.doi: 10.3969/j.issn.1004-4132.2010.06.002

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Super-resolution image reconstruction based on three-step-training neural networks

Fuzhen Zhu*,Jinzong Li,Bing Zhu,and Dongdong Ma   

  1. Institute of Image Information Technology and Engineering, Harbin Institute of Technology, Heilongjiang 150001, P. R. China
  • Online:2010-12-20 Published:2010-01-03


A new method of super-resolution image reconstruction is proposed, which uses a three-step-training error backpropagation neural network (BPNN) to realize the super-resolution reconstruction (SRR) of satellite image. The method is based on BPNN. First, three groups learning samples with different resolutions are obtained according to image observation model, and then vector mappings are respectively used to those three group
learning samples to speed up the convergence of BPNN, at last, three times consecutive training are carried on the BPNN. Training samples used in each step are of higher resolution than those used in the previous steps, so the increasing weights store a great amount of information for SRR, and network performance and generalization ability are improved greatly. Simulation and generalization tests are carried on the well-trained three-step-training NN respectively, and the reconstruction results with higher resolution images verify the effectiveness and validity of this method.

Key words: image reconstruction, super-resolution, three-steptraining neural network, BP algorithm, vector mapping