  Reference-based image super-resolution (RefSR) is a promising SR branch and
has shown great potential in overcoming the limitations of single image
super-resolution. While previous state-of-the-art RefSR methods mainly focus on
improving the efficacy and robustness of reference feature transfer, it is
generally overlooked that a well reconstructed SR image should enable better SR
reconstruction for its similar LR images when it is referred to as. Therefore,
in this work, we propose a reciprocal learning framework that can appropriately
leverage such a fact to reinforce the learning of a RefSR network. Besides, we
deliberately design a progressive feature alignment and selection module for
further improving the RefSR task. The newly proposed module aligns
reference-input images at multi-scale feature spaces and performs
reference-aware feature selection in a progressive manner, thus more precise
reference features can be transferred into the input features and the network
capability is enhanced. Our reciprocal learning paradigm is model-agnostic and
it can be applied to arbitrary RefSR models. We empirically show that multiple
recent state-of-the-art RefSR models can be consistently improved with our
reciprocal learning paradigm. Furthermore, our proposed model together with the
reciprocal learning strategy sets new state-of-the-art performances on multiple
benchmarks.
