Radial Basis Function Neural Network Based Super- Resolution Restoration for an Undersampled Image  

作　　者：苏秉华 金伟其 牛丽红 

机构地区：[1]School of Information Science and Technology, Beijing Institute of Technology, Beijing100081, China [2]Institute of Optoelectronics, Shenzhen University, Shenzhen, Guandong518060, China

出　　处：《Journal of Beijing Institute of Technology》2004年第2期135-138,共4页北京理工大学学报（英文版）

基　　金：SponsoredbyFundforResearchonDoctoralProgramsinInstitutionsofHigherLearning ( 2 0 0 2 0 0 70 0 6)andBasicResearchFundofBIT (BIT UBF 2 0 0 3 0 1F2 0 )

摘　　要：To achieve restoration of high frequency information for an undersampled and degraded low-resolution image, a nonlinear and real-time processing method-the radial basis function (RBF) neural network based super-resolution method of restoration is proposed. The RBF network configuration and processing method is suitable for a high resolution restoration from an undersampled low-resolution image. The soft-competition learning scheme based on the k-means algorithm is used, and can achieve higher mapping approximation accuracy without increase in the network size. Experiments showed that the proposed algorithm can achieve a super-resolution restored image from an undersampled and degraded low-resolution image, and requires a shorter training time when compared with the multiplayer perception (MLP) network.To achieve restoration of high frequency information for an undersampled and degraded low-resolution image, a nonlinear and real-time processing method-the radial basis function (RBF) neural network based super-resolution method of restoration is proposed. The RBF network configuration and processing method is suitable for a high resolution restoration from an undersampled low-resolution image. The soft-competition learning scheme based on the k-means algorithm is used, and can achieve higher mapping approximation accuracy without increase in the network size. Experiments showed that the proposed algorithm can achieve a super-resolution restored image from an undersampled and degraded low-resolution image, and requires a shorter training time when compared with the multiplayer perception (MLP) network.

关 键 词：SUPER-RESOLUTION image restoration image processing neural networks UNDERSAMPLING 

分 类 号：TN91173[电子电信—通信与信息系统]