  Reference-based super-resolution (RefSR) has made significant progress in
producing realistic textures using an external reference (Ref) image. However,
existing RefSR methods obtain high-quality correspondence matchings consuming
quadratic computation resources with respect to the input size, limiting its
application. Moreover, these approaches usually suffer from scale misalignments
between the low-resolution (LR) image and Ref image. In this paper, we propose
an Accelerated Multi-Scale Aggregation network (AMSA) for Reference-based
Super-Resolution, including Coarse-to-Fine Embedded PatchMatch (CFE-PatchMatch)
and Multi-Scale Dynamic Aggregation (MSDA) module. To improve matching
efficiency, we design a novel Embedded PatchMacth scheme with random samples
propagation, which involves end-to-end training with asymptotic linear
computational cost to the input size. To further reduce computational cost and
speed up convergence, we apply the coarse-to-fine strategy on Embedded
PatchMacth constituting CFE-PatchMatch. To fully leverage reference information
across multiple scales and enhance robustness to scale misalignment, we develop
the MSDA module consisting of Dynamic Aggregation and Multi-Scale Aggregation.
The Dynamic Aggregation corrects minor scale misalignment by dynamically
aggregating features, and the Multi-Scale Aggregation brings robustness to
large scale misalignment by fusing multi-scale information. Experimental
results show that the proposed AMSA achieves superior performance over
state-of-the-art approaches on both quantitative and qualitative evaluations.
