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CNNs in the frequency domain for image super-resolution
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Paper Abstract

This paper develops methods for recovering high-resolution images from low-resolution images by combining ideas inspired by sparse coding, such as compressive sensing techniques, with super-resolution neural networks. Sparse coding leverages the existence of bases in which signals can be sparsely represented, and herein we use such ideas to improve the performance of super-resolution convolutional neural networks (CNN). In particular, we propose an improved model in which CNNs are used for super-resolution in the frequency domain, and we demonstrate that such an approach improves the performance of image super-resolution neural networks. In addition, we indicate that instead of numerous deep layers, a shallower architecture in the frequency domain is sufficient for many types of image super-resolution problems.

Paper Details

Date Published: 27 November 2019
PDF: 6 pages
Proc. SPIE 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence, 113210D (27 November 2019); doi: 10.1117/12.2539288
Show Author Affiliations
Yingnan Liu, Worcester Polytechnic Institute (United States)
Randy Clinton Paffenroth, Worcester Polytechnic Institute (United States)


Published in SPIE Proceedings Vol. 11321:
2019 International Conference on Image and Video Processing, and Artificial Intelligence
Ruidan Su, Editor(s)

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