  Recently, there is a vast interest in developing methods which are
independent of the training samples such as deep image prior, zero-shot
learning, and internal learning. The methods above are based on the common goal
of maximizing image features learning from a single image despite inherent
technical diversity. In this work, we bridge the gap between the various
unsupervised approaches above and propose a general framework for image
restoration and image retargeting. We use contextual feature learning and
internal learning to improvise the structure similarity between the source and
the target images. We perform image resize application in the following setups:
classical image resize using super-resolution, a challenging image resize where
the low-resolution image contains noise, and content-aware image resize using
image retargeting. We also provide comparisons to the relevant state-of-the-art
methods.
