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Recent Reference-Based image super-resolution (RefSR) has improved SOTA deepmethods introducing attention mechanisms to enhance low-resolution images bytransferring high-resolution textures from a reference high-resolution image.The main idea is to search for matches between patches using LR and Referenceimage pair in a feature space and merge them using deep architectures. However,existing methods lack the accurate search of textures. They divide images intoas many patches as possible, resulting in inefficient memory usage, and cannotmanage large images. Herein, we propose a deep search with a more efficientmemory usage that reduces significantly the number of image patches and findsthe $k$ most relevant texture match for each low-resolution patch over thehigh-resolution reference patches, resulting in an accurate texture match. Weenhance the Super Resolution result adding gradient density information using asimple residual architecture showing competitive metrics results: PSNR andSSMI.

EXTRACTER: Efficient Texture Matching with Attention and Gradient Enhancing for Large Scale Image Super Resolution