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.

通过引入两个包含非对焦散焦的新数据集 NYUv2-BSR 和 Cityscapes-BSR，分析不具有空间不变性的空间变异性使传统的盲超分辨率 (SR) 方法性能严重下降，并设计了一种新的 CMOS 网络来同时估计图像的位置不变性和语境，实验结果表明了其方法优于目前技术的传统方法。