Image Super-Resolution Using Residual Global Context Network
Recent studies have showed that convolutional neural networks (CNN) can effectively improve the performance of single image super-resolution (SR). However, previous methods rarely considered long-range dependencies between pixels and channel-wise interdependencies at the same time. They ignores the...
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Published in | ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 2633 - 2637 |
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Main Authors | , , , , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
01.05.2020
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Subjects | |
Online Access | Get full text |
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Summary: | Recent studies have showed that convolutional neural networks (CNN) can effectively improve the performance of single image super-resolution (SR). However, previous methods rarely considered long-range dependencies between pixels and channel-wise interdependencies at the same time. They ignores the fact that natural images have strong internal data repetition which requires the network to capture long-range dependencies between pixels and considering the interdepen-dencies between channels can better exploit the input information of the network. In addition, although past studies have proved that deep convolutional neural network benefit the performance of image super-resolution, it also means that the network needs more memory consumption and higher computational complexity. To solve these problem,we introduce Global Context block (GCB) and design a comparative shallow network called Residual Global Context Networks (RGC-N). It achieves a better trade-off between the amount of parameter and the quality of image reconstruction. Extensive experiments demonstrate that the proposed method is superior to the state-of-the-art methods. |
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ISSN: | 2379-190X |
DOI: | 10.1109/ICASSP40776.2020.9054003 |