Reference-Based Face Super-Resolution Using the Spatial Transformer
arXiv:2607.11025 · doi:10.1007/978-981-96-0911-6_24
The paper introduces a reference-based face super-resolution method that leverages high‑resolution reference images and a spatial transformer alignment module to improve detail recovery, achieving state‑of‑the‑art performance with a relatively small model.
Abstract
Face super-resolution is the task of increasing the resolution of an image containing a face thereby adding finer detail. It is a ubiquitous task in many computer vision applications and quite often the user isn't even aware that it is being performed. However, doing it with high fidelity is challenging as it is an ill-posed problem. In this paper we present a reference-based solution for face super-resolution that uses higher resolution reference images to aid in the task. We show an alignment module based on the spatial transformer that is considerably more stable than the popular deformable convolutions. We also show an aggregation function that can take good quality information from the reference images when available or suppress the function when such information is unavailable. Finally, we show that our relatively smaller model can achieve state of the art results on multiple datasets. The source code is available at https://github.com/varun-jois/FSRST.