Multi-frame Image Super-Resolution Algorithm Based on Small Amount of Data

Abstract

In this paper, a novel multi-frame image super-resolution algorithm for small amount of data is proposed. Our method solve the problem that the spatial resolution of the reconstructed image is low and the visual quality of it is poor when the number of input low-resolution images is small. In order to improve the quality of the initial estimation, we construct the initial estimation with multi-frame low-resolution images according to the registration parameter and interpolate the missing pixels by directional Gaussian-like filtering. In order to solve the problem of fuzzy initial estimation, the enhancement method is used to highlight the image details. A large number of qualitative and quantitative evaluation results show that our method has strong reconstruction performance for various types of low-resolution images under different amount of data.