Super-resolution image reconstruction based on three-step-training neural networks  

作　　者：Fuzhen Zhu Jinzong Li Bing Zhu Dongdong Ma 

机构地区：[1]Institute of Image Information Technology and Engineering, Harbin Institute of Technology, Heilongjiang 150001, R R. China

出　　处：《Journal of Systems Engineering and Electronics》2010年第6期934-940,共7页系统工程与电子技术（英文版）

摘　　要：A new method of super-resolution image reconstruction is proposed, which uses a three-step-training error backpropagation neural network （BPNN） to realize the super-resolution reconstruction （SRR） of satellite image. The method is based on BPNN. First, three groups learning samples with different resolutions are obtained according to image observation model, and then vector mappings are respectively used to those three group learning samples to speed up the convergence of BPNN, at last, three times consecutive training are carried on the BPNN. Training samples used in each step are of higher resolution than those used in the previous steps, so the increasing weights store a great amount of information for SRR, and network performance and generalization ability are improved greatly. Simulation and generalization tests are carried on the well-trained three-step-training NN respectively, and the reconstruction results with higher resolution images verify the effectiveness and validity of this method.A new method of super-resolution image reconstruction is proposed, which uses a three-step-training error backpropagation neural network （BPNN） to realize the super-resolution reconstruction （SRR） of satellite image. The method is based on BPNN. First, three groups learning samples with different resolutions are obtained according to image observation model, and then vector mappings are respectively used to those three group learning samples to speed up the convergence of BPNN, at last, three times consecutive training are carried on the BPNN. Training samples used in each step are of higher resolution than those used in the previous steps, so the increasing weights store a great amount of information for SRR, and network performance and generalization ability are improved greatly. Simulation and generalization tests are carried on the well-trained three-step-training NN respectively, and the reconstruction results with higher resolution images verify the effectiveness and validity of this method.

关 键 词：image reconstruction SUPER-RESOLUTION three-steptraining neural network BP algorithm vector mapping. 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TE626.88[自动化与计算机技术—控制科学与工程]