关键词: 超分辨, 非均匀插值, 径向基函数, 解模糊

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