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Dynamic dual attention iterative network for image super-resolution

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Abstract

Recently, deep convolution neural networks (DCNNs) have obtained remarkable performance in exploring single image super-resolution (SISR). However, most of the existing CNN-based SISR methods only focus on increasing the width and depth of the network to improve SR performance, which makes them face a heavy computing burden. In this paper, we propose a lightweight dynamic dual attention iteration network (DDAIN) for SISR. Specifically, to better realize the attention of the channel and the convolution kernel, we design a dynamic convolution unit (DYCU) at the head of the network. It improves the SR performance by enhancing the complexity of the model without increasing the width and depth of the network. Compared with the traditional static convolution, it can extract more abundant high and low-frequency image features according to different input images. Moreover, to recover the high-frequency detail features of images with different resolutions as much as possible, we embed multiple dual residual attention (DRA) in the feature refinement unit (FRU). Finally, to alleviate the height discomfort caused by SR, we introduce iterative loss Liter to optimize the training process further. Extensive experimental results on benchmark show that the performance of the DDAIN in different degradation models exceeds some existing classical methods.

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Acknowledgements

This research was funded in part by the Natural Science Foundation of Xinjiang Uygur Autonomous Region grant number 2020D01C034, Tianshan Innovation Team of Xinjiang Uygur Autonomous Region grant number 2020D14044, the National Science Foundation of China under Grant U1903213, 61771416 and 62041110, the Creative Research Groups of Higher Education of Xinjiang Uygur Autonomous Region under Grant XJEDU2017T002.

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  1. School of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China

    Hao Feng, Liejun Wang, Shuli Cheng, Anyu Du & Yongming Li

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  1. Hao Feng
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Correspondence to Liejun Wang.

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Feng, H., Wang, L., Cheng, S. et al. Dynamic dual attention iterative network for image super-resolution. Appl Intell 52, 8189–8208 (2022). https://doi.org/10.1007/s10489-021-02816-2

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