Remote Sensing Images Super-resolution Reconstruction Based on Bayesian Nonparametric Dictionary Learning

doi:10.13474/j.cnki.11-2246.2018.0199

Abstract

Abstract: Aiming at taking advantage of the prior information of the samples and the parameters,a single remote sensing image super-resolution method based on nonparametric Bayesian dictionary learning is proposed,modified on the sparse representation algorithm presented by Yang.The Beta-Bernoulli process was used to learn the dictionary,establishing the probability distribution models of dictionary elements and parameters,and the stationary distribution of the Markov chain obtained by Gibbs iteration sampling was used to approximate their posterior.Finally,the related high resolution images were reconstructed by the posterior and the low resolution images.Compared with the bilinear,the bicubic and the super-resolution algorithm based on sparse representation, the average peak signal-to-noise ratio obtained using the method presented in this paper were increased by 3.29,1.79, 0.17 dB,respectively.the average ERGAS were decreased by 0.78, 0.37, 0.02 dB,respectively.Because of more prior added,the reconstruction image produced using the proposed method could provide more high frequency details,performing universally.

Key words: super-resolution, remote sensing images, sparse representation, nonparametric Bayesian, probability distribution

CLC Number:

P237

LI Li, SUI Lichun, KANG Junmei, WANG Xue. Remote Sensing Images Super-resolution Reconstruction Based on Bayesian Nonparametric Dictionary Learning[J]. 测绘通报, 2018, 0(7): 5-8,12.

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