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Recently, there is a vast interest in developing methods which areindependent of the training samples such as deep image prior, zero-shotlearning, and internal learning. The methods above are based on the common goalof maximizing image features learning from a single image despite inherenttechnical diversity. In this work, we bridge the gap between the variousunsupervised approaches above and propose a general framework for imagerestoration and image retargeting. We use contextual feature learning andinternal learning to improvise the structure similarity between the source andthe target images. We perform image resize application in the following setups:classical image resize using super-resolution, a challenging image resize wherethe low-resolution image contains noise, and content-aware image resize usingimage retargeting. We also provide comparisons to the relevant state-of-the-artmethods.

DCIL: Deep Contextual Internal Learning for Image Restoration and Image Retargeting