A two-stage blind image super-resolution algorithm based on kernel estimation

Previous blind image super-resolution (BISR) approaches based on iterative methods tend to be relatively time-consuming and cause large amounts of computation, to address the problem, in this paper, we divide BISR into two stages: firstly, pre-kernels are pre-estimated and pre-SR images are pre-restored from LR images. After that, based on the features of different depths in the pre-SR network, we obtain the more accurate blur kernels, which help restore better SR images. We also improve the SR network with single way of fusion degradation by designing expert modules (EM) to deal with multiple degradation and spatial-channel fusion method to deepen the fusion of the degradation information and features.