Multi-Reference Image Super-Resolution: A Posterior Fusion Approach
This work addresses image quality enhancement for computer vision applications, but it is incremental as it builds on existing reference-based super-resolution methods.
The paper tackles the ill-posed problem of image super-resolution by proposing a 2-step-weighting posterior fusion approach to combine outputs from multiple reference images, achieving consistent improvements in image quality on the CUFED5 dataset.
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.