Image Super-Resolution Restoration Instance-Based Learning and Iterative Kernel Regression

Abstract

This paper proposes an image super-resolution restoration algorithm based on example learning and iterative directional kernel regression, which is used to solve the problem that the existing super-resolution restoration algorithm based on example learning cannot effect the restored image has a small degree of matching with the sample library and the problem of image restoration in the presence of noise. Among them, the example learning can achieve basic image restoration. In the directional kernel regression, the estimated smoothing matrix can obtain the minimum mean square estimation after multiple iterations, and further optimize the super-resolution restored image. Simulation results show that the improved algorithm improves the robustness and edge preservation characteristics of the super-resolution restoration image. Compared with the classical algorithm, the algorithm has better visual effects and the root mean square error can be improved over 15%, and reflect the effectiveness of the algorithm.