资源三号卫星影像超分辨率重建

doi:10.3969/j.issn.0255-8297.2015.03.009

Abstract

Abstract: Two refactoring approaches, one based on iterative back projection (IBP) and
the other based on sparse representation and dictionary learning, are discussed. Three
linear array images of the ZY-3 satellite are used to reconstruct super-resolution images.
The reconstruction results are evaluated according to four objective criteria, i.e., mean
brightness, PSNR, information entropy, and sharpness of images. The results obtained with
the two approaches show that the sparse representation and dictionary learning method is
better than the iterative back projection method.

Key words: super-resolution, feature matching, iterative back projection (IBP), dictionary learning, sparse representation

CLC Number:

P237.3

JIA Yong-hong1,2, Lü Zhen1, ZHOU Ming-ting1. Super Resolution Reconstruction of ZY-3 Satellite Images[J]. Journal of Applied Sciences, 2015, 33(3): 309-316.

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Super Resolution Reconstruction of ZY-3 Satellite Images

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