EXTRACTER: Efficient Texture Matching with Attention and Gradient
  Enhancing for Large Scale Image Super Resolution
Esteban Reyes-Saldana Mariano Rivera
Recent Reference-Based image super-resolution (RefSR) has improved SOTA deep
methods introducing attention mechanisms to enhance low-resolution images by
transferring high-resolution textures from a reference high-resolution image.
The main idea is to search for matches between patches using LR and Reference
image pair in a feature space and merge them using deep architectures. However,
existing methods lack the accurate search of textures. They divide images into
as many patches as possible, resulting in inefficient memory usage, and cannot
manage large images. Herein, we propose a deep search with a more efficient
memory usage that reduces significantly the number of image patches and finds
the $k$ most relevant texture match for each low-resolution patch over the
high-resolution reference patches, resulting in an accurate texture match. We
enhance the Super Resolution result adding gradient density information using a
simple residual architecture showing competitive metrics results: PSNR and
SSMI.