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SCCADC-SR: a real image super-resolution based on self-calibration convolution and adaptive dense connection

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Abstract

Because the real degradation model is more complex, and the different computing performance of devices leads to different degradation results. The super-resolution based on the real image degradation model has great challenges in practical applications. To solve these problems, we propose a novel SR network based on self-calibration convolution and adaptive dense connection (SCCADC-SR). Firstly, we introduce self-calibration convolution as the basic convolution module and use it as a supplement to the attention mechanism. Secondly, we use efficient channel attention (ECA) to construct an adaptive dense connection structure to deal with the features at the different levels. Then, we use the CutBlur method to enhance the data to improve the generalization ability of the model and use the long skip connection to improve the convergence of the depth model structure. Finally, SCCADC-SR combines self-ensemble and model ensemble to improve the model’s robustness and reduce the noise. Experimental results show that for both real image data and Bicubic data, our SCCADC-SR improves SR reconstruction performance by 5% compared with the state-of-the-art methods.

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Funding

This research was supported by the National Natural Science Foundation of China (61573182, 62073164), and by the Fundamental Research Funds for the Central Universities (NS2022041).

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Authors and Affiliations

  1. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China

    Xin Yang, Hengrui Li, Chenhuan Wu & Tao Li

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  1. Xin Yang
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  2. Hengrui Li
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Correspondence to Xin Yang.

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Xin Yang declares that he has no conflict of interest, Hengrui Li declares that he has no conflict of interest. Chenhuan Wu declares that he has no conflict of interest, Tao Li declares that he has no conflict of interest.

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Yang, X., Li, H., Wu, C. et al. SCCADC-SR: a real image super-resolution based on self-calibration convolution and adaptive dense connection. Multimed Tools Appl 82, 45699–45716 (2023). https://doi.org/10.1007/s11042-023-15481-z

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