关键词: 超分辨率重建, 非下采样Contourlet变换, 改进BP神经网络

Abstract: This paper presents a new learning based super-resolution of image by introducing the Nonsubsampled Contourlet Transform（NSCT） and improved BP neural network.NSCT can recover the detail information better, as the improved BP can simulate the highly nonlinear, which is fast convergence and accuracy. For both super-resolution image and low-resolution image, it extracts the Contourlet coefficients of each sub-band training the improved BP network, then using the stable and restraining network realizes super-resolution reconstruction of image. The results show that this method is able to preserve the details of original image better and reduce the complexity of the network reconstruction at the same time, raise the accuracy, get significantly improved in reconstruction results.

Key words: super-resolution reconstruction, nonsubsampled Contourlet transform, improved BP neural network