Abstract
Single Image Super-Resolution techniques have the function of retrieving a high resolution image from a single low resolution input. They implement deep learning heuristics which perform the techniques to form pixel-accourate reproductions. In this paper we have experimented upon various neural architectures with unique approaches towards the task of super-resolution. We have especially elaborated upon adversarial training networks which are yielding progressive results in both conditional and quantifiable benchmarks.
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Adate, A., Tripathy, B.K. (2019). Understanding Single Image Super-Resolution Techniques with Generative Adversarial Networks. In: Bansal, J., Das, K., Nagar, A., Deep, K., Ojha, A. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 816. Springer, Singapore. https://doi.org/10.1007/978-981-13-1592-3_66
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DOI: https://doi.org/10.1007/978-981-13-1592-3_66
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