You can also find the article on arxiv: https://arxiv.org/abs/2209.13131

Not exactly. Both are formulated as inverse problem in image processing. Super-Resolution investigates the case where information is lost due to downscaling whereas deblurring focus on blurry input (e.g., by low pass filters). However, they have similar properties and deep learning based methods can be applied to both. In this survey, we didn't go deeper into the deblurring topic.

Thank you very much for your comment. It is a very valuable and important note for the subject and community as this is a super important aspect of image SR. We refer to this topic under the Unsupervised SR section (8) but did not have the space to go into more detail, which doesn't mean it doesn't deserve attention. We referenced another survey by Liu et al. (“Blind image superresolution: A survey and beyond", https://arxiv.org/abs/2107.03055) from 2022 to fill this gap (also mentions KernelGAN and related methods), which we find is an informative source for blind SR in general.