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YUVDR: A residual network for image deblurring in YUV color space

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Abstract

Motion blur removal caused by camera shake and object motion in 3D space has long been a challenge in computer vision. Although RGB images are commonly used as input data for CNN-based image deblurring, their inherent issues of color overlap and high dimensionality can limit performance. To address these problems, we propose YUVDR, a residual network based on YUV color space, for image deblurring. By using YUV images, we mitigate the issues of color overlap and mutual influence. We introduce novel loss functions and conduct experiments on three datasets, namely GoPro, DVD and NFS, which offer a wide range of image quality levels, scene complexities, and types of motion blur. Our proposed method outperforms state-of-the-art algorithms, yielding a 3-5 dB improvement in the PSNR of test results. In addition, utilizing the YUV color space as the input data can greatly reduce the number of training parameters and model size, by approximately 15 times. This optimization of GPU memory not only improves training efficiency, but also reduces testing time for practical applications.

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Data availability statement

The datasets generated during and/or analysed during the current study are available, visit https://github.com/VITA-Group/DeblurGANv2#datasets. The links to download each dataset are provided below: GoPro https://drive.google.com/file/d/1KStHiZn5TNm2mo3OLZLjnRvd0vVFCI0W/view, DVD https://drive.google.com/file/d/1bpj9pCcZR_6-AHb5aNnev5lILQbH8GMZ/view, NFS https://drive.google.com/file/d/1Ut7qbQOrsTZCUJA_mJLptRMipD8sJzjy/view

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Acknowledgements

We would like to express our sincere gratitude to the anonymous reviewers for their insightful and constructive comments, which helped to improve the quality of this article. We would also like to thank the editor for their guidance and valuable feedback throughout the submission process

Funding

This work was supported by National Natural Science Foundation of China under Grant 61301250, China Scholarship Council under Grant [2020]1417, Natural Science Foundation for Young Scientists of Shanxi Province under Grant 201901D211313, Shanxi Scholarship Council of China under Grant HGKY2019080 and 2020-127, Open project of Guangdong Provincial Key Laboratory of Digital Signal and Image Processing in 2021, Shanxi Province Postgraduate Excellent Innovation Project Plan under Grant 2021Y679

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  1. School of Electronics and Information Engineering, Taiyuan University of Science and Technology, Taiyuan, Shanxi, 030024, China

    Meng Zhang, Haidong Wang & Yina Guo

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  1. Meng Zhang
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  2. Haidong Wang
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Correspondence to Yina Guo.

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Zhang, M., Wang, H. & Guo, Y. YUVDR: A residual network for image deblurring in YUV color space. Multimed Tools Appl 83, 19541–19561 (2024). https://doi.org/10.1007/s11042-023-16284-y

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