  Super-Resolution is the technique to improve the quality of a low-resolution
photo by boosting its plausible resolution. The computer vision community has
extensively explored the area of Super-Resolution. However, previous
Super-Resolution methods require vast amounts of data for training which
becomes problematic in domains where very few low-resolution, high-resolution
pairs might be available. One such area is statistical downscaling, where
super-resolution is increasingly being used to obtain high-resolution climate
information from low-resolution data. Acquiring high-resolution climate data is
extremely expensive and challenging. To reduce the cost of generating
high-resolution climate information, Super-Resolution algorithms should be able
to train with a limited number of low-resolution, high-resolution pairs. This
paper tries to solve the aforementioned problem by introducing a
semi-supervised way to perform super-resolution that can generate sharp,
high-resolution images with as few as 500 paired examples. The proposed
semi-supervised technique can be used as a plug-and-play module with any
supervised GAN-based Super-Resolution method to enhance its performance. We
quantitatively and qualitatively analyze the performance of the proposed model
and compare it with completely supervised methods as well as other unsupervised
techniques. Comprehensive evaluations show the superiority of our method over
other methods on different metrics. We also offer the applicability of our
approach in statistical downscaling to obtain high-resolution climate images.
