  Existing blind image super-resolution (SR) methods mostly assume blur kernels
are spatially invariant across the whole image. However, such an assumption is
rarely applicable for real images whose blur kernels are usually spatially
variant due to factors such as object motion and out-of-focus. Hence, existing
blind SR methods would inevitably give rise to poor performance in real
applications. To address this issue, this paper proposes a mutual affine
network (MANet) for spatially variant kernel estimation. Specifically, MANet
has two distinctive features. First, it has a moderate receptive field so as to
keep the locality of degradation. Second, it involves a new mutual affine
convolution (MAConv) layer that enhances feature expressiveness without
increasing receptive field, model size and computation burden. This is made
possible through exploiting channel interdependence, which applies each channel
split with an affine transformation module whose input are the rest channel
splits. Extensive experiments on synthetic and real images show that the
proposed MANet not only performs favorably for both spatially variant and
invariant kernel estimation, but also leads to state-of-the-art blind SR
performance when combined with non-blind SR methods.
