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Image color rendering based on frequency channel attention GAN

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Abstract

In recent years, channel attention mechanism has greatly improved the performance of computer vision-oriented network models. But the simple superposition of modules inevitably increases the complexity of the model. In order to improve the performance and reduce the complexity of the model, a novel frequency channel attention GAN is proposed and applied to image color rendering. Firstly, global average pooling is a special case of discrete cosine transform. In order to better capture the rich input mode information, we extend global mean pooling to the frequency domain to obtain the frequency channel attention mechanism. Secondly, the frequency channel attention mechanism is combined with U-Net network to represent all the feature information of the image. The effectiveness of channel attention GAN in frequency domain was verified by using DIV2K dataset and COCO dataset. Finally, compared with pix2pix, CycleGAN, and HCEGAN models, PSNR increased by 2.660 dB, 2.595 dB and 1.430 dB, and SSIM increased by 7.943%, 6.790% and 2.436%. Experimental results show that our method not only improves the image rendering effect and quality, but also enhances the model stability.

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Acknowledgements

This work was partly supported by the Natural Science Basis Research Plan in Shaanxi Province of China under Grant 2023-JC-YB-517 and the Open Project Program of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University under Grant VRLAB2023B08, and the high-level talent introduction project of Shaanxi Technical College of Finance & Economics under Grant 2022KY01. All of the authors declare that there is no conflict of interest regarding the publication of this article and would like to thank the anonymous referees for their valuable comments and suggestions.

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Author notes

  1. D. Wang, M. Zhang and J. Liu have contributed equally to this work.

Authors and Affiliations

  1. College of Computer Science and Technology, Xi’an University of Science and Technology, Xi’an, 710054, China

    Hong-an Li, Diao Wang & Min Zhang

  2. State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, 100191, China

    Hong-an Li

  3. Xi’an Xiangteng Microelectronics Technology Co., Ltd, Xi’an, 710018, China

    Min Zhang

  4. Shaanxi Technical College of Finance and Economics, Xianyang, 712099, China

    Jun Liu

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  1. Hong-an Li
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These authors contributed equally to this work.

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Correspondence to Diao Wang.

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Li, Ha., Wang, D., Zhang, M. et al. Image color rendering based on frequency channel attention GAN. SIViP 18, 3179–3186 (2024). https://doi.org/10.1007/s11760-023-02980-7

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  • DOI: https://doi.org/10.1007/s11760-023-02980-7

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