基于多类预测器学习的超分辨率复原算法

摘要

To solve the problems of image mismatching and smooth edges in pre-classified based super-resolution algorithms,a non-linear multi-class prediction learning algorithm based on neural network was proposed.The neural network multi-class predictor was designed,and niche gene expression programming was used to optimize the back-propagation neural network.The predictor was trained by learning samples and prior knowledge of samples was obtained to predict high frequency information of image and further to complete the super-resolution image restoration.Experimental results show that compared with sample preclassified based algorithms,PSNR and SSIM of the proposed algorithm are improved respectively,and subjectively the restoration results are more abundant in detail.%为解决样本学习超分辨率算法的图像样本误匹配和边缘平滑问题,提出一种基于神经网络的非线性多类预测器学习算法,设计神经网络多类预测器,采用小生境基因表达式编程方法优化反向传播神经网络.通过学习样本集对预测器进行训练,学得学习样本中的先验知识,根据从低分辨率图像块提取的特征矢量预测图像高频信息,完成图像超分辨率复原.实验结果表明,相比样本预分类学习的几种算法,该算法的PSNR和SSIM值均有了一定提升,主观上复原结果具有更丰富的细节.