  It is widely agreed that reference-based super-resolution (RefSR) achieves
superior results by referring to similar high quality images, compared to
single image super-resolution (SISR). Intuitively, the more references, the
better performance. However, previous RefSR methods have all focused on
single-reference image training, while multiple reference images are often
available in testing or practical applications. The root cause of such
training-testing mismatch is the absence of publicly available multi-reference
SR training datasets, which greatly hinders research efforts on multi-reference
super-resolution. To this end, we construct a large-scale, multi-reference
super-resolution dataset, named LMR. It contains 112,142 groups of 300x300
training images, which is 10x of the existing largest RefSR dataset. The image
size is also much larger. More importantly, each group is equipped with 5
reference images with different similarity levels. Furthermore, we propose a
new baseline method for multi-reference super-resolution: MRefSR, including a
Multi-Reference Attention Module (MAM) for feature fusion of an arbitrary
number of reference images, and a Spatial Aware Filtering Module (SAFM) for the
fused feature selection. The proposed MRefSR achieves significant improvements
over state-of-the-art approaches on both quantitative and qualitative
evaluations. Our code and data would be made available soon.
