Computer Science ›› 2018, Vol. 45 ›› Issue (11): 267-271.doi: 10.11896/j.issn.1002-137X.2018.11.042

• Graphics, Image & Pattern Recognition • Previous Articles     Next Articles

Image Reconstruction Based on Supervised Learning Deep Auto-encoder

ZHANG Sai1, RUI Ting1,2, REN Tong-wei2, YANG Cheng-song1, ZOU Jun-hua1   

  1. (Department of Filed Engineering,PLA Army Engineering University,Nanjing 210007,China)1
    (State Key Laboratory for Novel Software Technology,Nanjing University,Nanjing 210023,China)2
  • Received:2017-10-11 Published:2019-02-25

Abstract: Aiming at the reconstruction of the damaged information of digital image,this paper proposed a new approach in which the classical unsupervised auto-encoder(AE) is used for supervised learning,and researched the deep model structure and training strategy.Specifically,this paper presented a novel supervised learning based deep auto-encoder model which possesses a set of progressive and interrelated learning strategies through designing multiple groups of supervised single-layer AE.In the novel model,the one-to-one training strategy in classical AE model (one output corresponding to one input) is substituted by the many-to-one training strategy (one output corresponding to many inputs).Then,the structure and training strategy mentioned above were utilized for the damaged or occluded images to test the process of data reconstruction,thus improving the model’s ability to express and reconstruct the feature of those data.Experimental results show that the new method has good reconstruction effect and adaptability to the damaged or occluded samples.

Key words: Auto-encoder, Deep structure, Image reconstruction, Supervised learning, Training strategy

CLC Number: 

  • TP391
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