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Image super-resolution using progressive residual multi-dilated aggregation network

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Abstract

Recently, single image super-resolution based on convolutional neural network (CNN) has achieved considerable improvements against traditional methods. However, it is still challenging for most CNN-based methods to obtain satisfactory reconstruction quality for large-scale factors. To solve the issues, we propose a progressive residual multi-dilated aggregation network (PRMAN), which performs multi-level \(\times \)2 upsampling to reconstruct images with large-scale factors. Specially, we design a residual multi-dilated aggregation block to simplify the model and supply enriched features with different receptive fields. Simultaneously, the channel attention mechanism is adopted to select informative features. Furthermore, to speed up the convergence and attain better performance, we train the model with two-stage training strategy. Extensive experimental results show that our proposed PRMAN exceeds the state-of-the-art methods in most cases.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant 61971306, 61520106002, 61471262.

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  1. School of Electrical and Information Engineering, Tianjin University, Tianjin, China, 300072

    Anqi Liu, Sumei Li & Yongli Chang

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  1. Anqi Liu
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  2. Sumei Li
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  3. Yongli Chang
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Correspondence to Sumei Li.

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Liu, A., Li, S. & Chang, Y. Image super-resolution using progressive residual multi-dilated aggregation network. SIViP 16, 1271–1279 (2022). https://doi.org/10.1007/s11760-021-02078-y

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