  The multi-scale receptive field and large kernel attention (LKA) module have
been shown to significantly improve performance in the lightweight image
super-resolution task. However, existing lightweight super-resolution (SR)
methods seldom pay attention to designing efficient building block with
multi-scale receptive field for local modeling, and their LKA modules face a
quadratic increase in computational and memory footprints as the convolutional
kernel size increases. To address the first issue, we propose the multi-scale
blueprint separable convolutions (MBSConv) as highly efficient building block
with multi-scale receptive field, it can focus on the learning for the
multi-scale information which is a vital component of discriminative
representation. As for the second issue, we revisit the key properties of LKA
in which we find that the adjacent direct interaction of local information and
long-distance dependencies is crucial to provide remarkable performance. Thus,
taking this into account and in order to mitigate the complexity of LKA, we
propose a large coordinate kernel attention (LCKA) module which decomposes the
2D convolutional kernels of the depth-wise convolutional layers in LKA into
horizontal and vertical 1-D kernels. LCKA enables the adjacent direct
interaction of local information and long-distance dependencies not only in the
horizontal direction but also in the vertical. Besides, LCKA allows for the
direct use of extremely large kernels in the depth-wise convolutional layers to
capture more contextual information, which helps to significantly improve the
reconstruction performance, and it incurs lower computational complexity and
memory footprints. Integrating MBSConv and LCKA, we propose a large coordinate
kernel attention network (LCAN).
