  Reference-based Super-resolution (RefSR) approaches have recently been
proposed to overcome the ill-posed problem of image super-resolution by
providing additional information from a high-resolution image. Multi-reference
super-resolution extends this approach by allowing more information to be
incorporated. This paper proposes a 2-step-weighting posterior fusion approach
to combine the outputs of RefSR models with multiple references. Extensive
experiments on the CUFED5 dataset demonstrate that the proposed methods can be
applied to various state-of-the-art RefSR models to get a consistent
improvement in image quality.
