Abstract: Neural networks have strong nonlinear learning ability, so the super-resolution algorithms based on neural networks are preliminarily studied. These algorithms can only be used in controlled microscanning, which has uniform displacement between frames. It is difficult to apply these algorithms to uncontrolled microscanning. In order to overcome the limiting condition and obtain better super-resolution performance, a deblurring algorithm using Radial Basis Function (RBF) neural network was firstly proposed, which was then combined with non-uniform interpolation step to form a new two-step super-resolution algorithm. The simulation results show that the Structural SIMilarity (SSIM) index of proposed algorithm is 0.55-0.7. The proposed two-step super-resolution algorithm not only extends application scope of RBF neural network but also achieves good super-resolution performance.

Key words: super-resolution, non-uniform interpolation, Radial Basis Function (RBF), deblurring

摘要： 神经网络具有强大的非线性学习能力,基于神经网络的多帧超分辨重建方法获得了初步研究,但这些方法一般只能应用于帧间具有标准位移的控制成像情形,难以推广应用到其他实际情况。为了将神经网络强大的学习能力应用到非控制成像多帧超分辨重建中,以获得更好的超分辨效果,提出了一种利用径向基函数(RBF)神经网络进行解模糊的算法,并将其与多帧非均匀插值结合起来,形成了一种新的两步超分辨算法。仿真实验结果表明,该算法的结构相似度为0.55～0.7。该算法不但扩展了RBF神经网络的应用范围，还获得了更好的超分辨性能。

关键词: 超分辨, 非均匀插值, 径向基函数, 解模糊