  Recent methods for single image super-resolution (SISR) have demonstrated
outstanding performance in generating high-resolution (HR) images from
low-resolution (LR) images. However, most of these methods show their
superiority using synthetically generated LR images, and their generalizability
to real-world images is often not satisfactory. In this paper, we pay attention
to two well-known strategies developed for robust super-resolution (SR), i.e.,
reference-based SR (RefSR) and zero-shot SR (ZSSR), and propose an integrated
solution, called reference-based zero-shot SR (RZSR). Following the principle
of ZSSR, we train an image-specific SR network at test time using training
samples extracted only from the input image itself. To advance ZSSR, we obtain
reference image patches with rich textures and high-frequency details which are
also extracted only from the input image using cross-scale matching. To this
end, we construct an internal reference dataset and retrieve reference image
patches from the dataset using depth information. Using LR patches and their
corresponding HR reference patches, we train a RefSR network that is embodied
with a non-local attention module. Experimental results demonstrate the
superiority of the proposed RZSR compared to the previous ZSSR methods and
robustness to unseen images compared to other fully supervised SISR methods.
