  The recent increase in the extensive use of digital imaging technologies has
brought with it a simultaneous demand for higher-resolution images. We develop
a novel edge-informed approach to single image super-resolution (SISR). The
SISR problem is reformulated as an image inpainting task. We use a two-stage
inpainting model as a baseline for super-resolution and show its effectiveness
for different scale factors (x2, x4, x8) compared to basic interpolation
schemes. This model is trained using a joint optimization of image contents
(texture and color) and structures (edges). Quantitative and qualitative
comparisons are included and the proposed model is compared with current
state-of-the-art techniques. We show that our method of decoupling structure
and texture reconstruction improves the quality of the final reconstructed
high-resolution image. Code and models available at:
https://github.com/knazeri/edge-informed-sisr
