Super-resolution image reconstruction based on RBF neural network

ZHU Fu-zhen; LI Jin-zong; ZHU Bing; LI Dong-dong; YANG Xue-feng

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Super-resolution image reconstruction based on RBF neural network

Article | 更新时间：2020-08-12

- Super-resolution image reconstruction based on RBF neural network
- Optics and Precision Engineering Vol. 18, Issue 6, Pages: 1444-1451(2010)
- 作者机构：
  
  哈尔滨工业大学图像信息与工程研究所,黑龙江哈尔滨 150001
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP183;TP391.4
- Received：02 July 2009，
  
  Revised：30 September 2009，
  
  Published Online：20 June 2010，
  
  Published：20 June 2010
- 稿件说明：
移动端阅览
朱福珍,李金宗,朱兵,李冬冬,杨学峰. 基于径向基函数神经网络的超分辨率图像重建[J]. 光学精密工程, 2010,18(6):1444-1451

ZHU Fu-zhen, LI Jin-zong, ZHU Bing, et al. Super-resolution image reconstruction based on RBF neural network[J]. Optics and precision engineering, 2010, 18(6): 1444-1451.
朱福珍,李金宗,朱兵,李冬冬,杨学峰. 基于径向基函数神经网络的超分辨率图像重建[J]. 光学精密工程, 2010,18(6):1444-1451 DOI：

ZHU Fu-zhen, LI Jin-zong, ZHU Bing, et al. Super-resolution image reconstruction based on RBF neural network[J]. Optics and precision engineering, 2010, 18(6): 1444-1451. DOI：

摘要

为了突破成像极限

经济可行地获取高质量的卫星图像

提出了一种基于径向基神经网络的超分辨率图像重建算法。以径向基神经网络为基础

依据卫星图像退化模型获取网络训练所需的学习样本图像

采用向量映射的方式加速网络收敛。其中

径向基函数的中心、宽度及网络的隐含层数、连接权值是决定径向基神经网络的关键参数

直接关系到网络的重建性能。采用最近邻聚类算法

动态地建立起基函数的中心及宽度

自适应地确定网络的隐含层数及连接权值。建立起的径向基函数神经网络显著地提高了图像重建性能和网络收敛速度(221 s即可收敛)。仿真实验和泛化实验表明

训练好的径向基神经网络可以有效地进行卫星图像的超分辨率重建

效率高

误差小。

Abstract

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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Related Author

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Related Institution

College of Electronic Engineering, Heilongjiang University

Post-Doctoral Research Station of Electronic Science and Technology, College of Electronic Engineering, Heilongjiang University

China Hebei Control Engineering Research Center

School of Artificial Intelligence， Hebei University of Technology

School of Computer Science and Technology，China University of Mining and Technology

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