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Fusing Attention Network Based on Dilated Convolution for Superresolution
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DOI：10.1109/TCDS.2022.3153090.png)
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Abstract
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Deep neural networks with different filters or multiple branches have achieved good performance for single superresolution (SR) in recent years. However, they ignore the high-frequency components of the multiscale context information of the low-resolution image. To solve this problem, we propose a fusing attention network based on dilated convolution (DFAN) for SR. Specifically, we first propose a dilated convolutional attention module (DCAM), which captures multiscale contextual information from different regions of LR images by locking multiple regions with different sizes of receptive fields. Then, we propose a multifeature attention block (MFAB), further focus on high-frequency components of multiscale contextual information, and extract more high-frequency features. Experimental results demonstrate that the proposed DFAN achieves performance improvements in terms of visual quality evaluation and quantitative evaluation.
Keywords:
Deep neural network
dilated convolutional attention module (DCAM)
high-frequency information
multifeature attention block (MFAB)
single superresolution (SR)
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