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Non-local Second-Order Attention Network for Single Image Super Resolution

Abstract : Single image super-resolution is a ill-posed problem which aims to characterize the texture pattern given a blurry and low-resolution image sample. Convolution neural network recently are introduced into super resolution to tackle this problem and further bringing forward progress in this field. Although state-of-the-art studies have obtain excellent performance by designing the structure and the way of connection in the convolution neural network, they ignore the use of high-order data to train more power model. In this paper, we propose a non-local second-order attention network for single image super resolution, which make the full use of the training data and further improve performance by non-local second-order attention. This attention scheme does not only provide a guideline to design the network, but also interpretable for super-resolution task. Extensive experiments and analyses have demonstrated our model exceed the state-of-the-arts models with similar parameters.
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https://hal.inria.fr/hal-03414726
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Submitted on : Thursday, November 4, 2021 - 3:57:07 PM
Last modification on : Friday, November 5, 2021 - 3:58:06 AM
Long-term archiving on: : Saturday, February 5, 2022 - 7:07:25 PM

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Jiawen Lyn, Sen Yan. Non-local Second-Order Attention Network for Single Image Super Resolution. 4th International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE), Aug 2020, Dublin, Ireland. pp.267-279, ⟨10.1007/978-3-030-57321-8_15⟩. ⟨hal-03414726⟩

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