Contents 1. Introduction
Single image super-resolution (SR) methods generate a high-resolution (HR) image from a single degraded low-resolution (LR) image. This ill-posed problem was initially solved using interpolation methods. However, SR is now commonly approached through the use of deep learning [6], [30], [45]. Image SR assumes that the LR image is obtained through a degradation processes. This can be expressed as: \begin{equation*}{\mathbf{y}} = ({\mathbf{x}}*{\mathbf{k}}){ \downarrow _s},\tag{1}\end{equation*} where * represents the convolution operation between the LR image and the blur kernel, and ↓s is the down-sampling operation with respective down-sampling factor ×s (e.g. ×2, ×3, ×4, ×8).
