  Deep learning-based super-resolution methods have shown great promise,
especially for single image super-resolution (SISR) tasks. Despite the
performance gain, these methods are limited due to their reliance on copious
data for model training. In addition, supervised SISR solutions rely on local
neighbourhood information focusing only on the feature learning processes for
the reconstruction of low-dimensional images. Moreover, they fail to capitalize
on global context due to their constrained receptive field. To combat these
challenges, this paper proposes a novel image-specific solution, namely
non-local variational autoencoder (\texttt{NLVAE}), to reconstruct a
high-resolution (HR) image from a single low-resolution (LR) image without the
need for any prior training. To harvest maximum details for various receptive
regions and high-quality synthetic images, \texttt{NLVAE} is introduced as a
self-supervised strategy that reconstructs high-resolution images using
disentangled information from the non-local neighbourhood. Experimental results
from seven benchmark datasets demonstrate the effectiveness of the
\texttt{NLVAE} model. Moreover, our proposed model outperforms a number of
baseline and state-of-the-art methods as confirmed through extensive
qualitative and quantitative evaluations.
