Super-resolution image reconstruction based on RBF neural network

被引：0
|
作者：
Zhu F.-Z. [1 ]
Li J.-Z. [1 ]
Zhu B. [1 ]
Li D.-D. [1 ]
Yang X.-F. [1 ]
机构：
[1] Institute of Image Information Technology and Engineering, Harbin Institute of Technology
来源：
Guangxue Jingmi Gongcheng/Optics and Precision Engineering | 2010年 / 18卷 / 06期
关键词：
Image reconstruction; RBF neural network; Super-resolution; The nearest neighbor clustering algorithm; Vector mapping;
D O I：
10.3788/OPE.20101806.1444
中图分类号：
学科分类号：
摘要：
In order to break through the limitations of imaging devices and to resolve the problems of Super-Resolution Reconstruction (SRR) of a satellite image, an image reconstruction based on the Radial Basis Function Neural Network (RBFNN) is proposed. First, learning sample images are acquired according to a satellite image observation model and the vector mapping is established to speed up the convergence of RBFNN. Then, the nearest neighbor clustering algorithm is used to dynamically establish the centers and widths of RBF, and decide adaptively the number of hidden layers and connection weights of a net, which are very important parameters for RBFNN. The method can improve the performance of SRR of satellite image and speed up the convergence of RBFNN to 221 s. Experimental results of simulation and generalization indicate that the well-trained RBFNN can realize the SRR of satellite images in higher spatial resolutions, higher efficiencies and lower errors.
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页码：1444 / 1451
页数：7
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