  Most of the existing blind image Super-Resolution (SR) methods assume that
the blur kernels are space-invariant. However, the blur involved in real
applications are usually space-variant due to object motion, out-of-focus,
etc., resulting in severe performance drop of the advanced SR methods. To
address this problem, we firstly introduce two new datasets with out-of-focus
blur, i.e., NYUv2-BSR and Cityscapes-BSR, to support further researches of
blind SR with space-variant blur. Based on the datasets, we design a novel
Cross-MOdal fuSion network (CMOS) that estimate both blur and semantics
simultaneously, which leads to improved SR results. It involves a feature
Grouping Interactive Attention (GIA) module to make the two modalities interact
more effectively and avoid inconsistency. GIA can also be used for the
interaction of other features because of the universality of its structure.
Qualitative and quantitative experiments compared with state-of-the-art methods
on above datasets and real-world images demonstrate the superiority of our
method, e.g., obtaining PSNR/SSIM by +1.91/+0.0048 on NYUv2-BSR than MANet.
