DCIL: Deep Contextual Internal Learning for image restoration and image retargeting

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dc.contributor.author Mastan, Indra Deep
dc.contributor.author Raman, Shanmuganathan
dc.date.accessioned 2019-12-18T12:44:35Z
dc.date.available 2019-12-18T12:44:35Z
dc.date.issued 2019-12
dc.identifier.citation Mastan, Indra Deep and Raman, Shanmuganathan, "DCIL: Deep Contextual Internal Learning for image restoration and image retargeting", arXiv, Cornell University Library, DOI: arXiv:1912.04229, Dec. 2019. en_US
dc.identifier.uri http://arxiv.org/abs/1912.04229
dc.identifier.uri https://repository.iitgn.ac.in/handle/123456789/5037
dc.description.abstract 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.
dc.description.statementofresponsibility by Indra Deep Mastan and Shanmuganathan Raman
dc.language.iso en_US en_US
dc.publisher Cornell University Library en_US
dc.title DCIL: Deep Contextual Internal Learning for image restoration and image retargeting en_US
dc.type Pre-Print en_US
dc.relation.journal arXiv


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