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Image Deblurring Using a Robust Loss Function

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Abstract

Deblurring images with outliers has always been a significantly challenging problem. Previous methods mainly involved complex operations, such as outlier and light streak detection, or sophisticated image priors for blur-kernel estimation, which increased the difficulty of deblurring images. Therefore, we developed a simple, yet efficient, blind deblurring algorithm in this study for handling images with outliers. To eliminate the impact of outliers during the kernel estimation process, we employed a robust Welsch loss function to characterize the data-fidelity term of our model. We observed that this function could extract significant edges successfully. Therefore, the image regularization term was also described by the same function. Using this unified robust function to describe our model can considerably reduce the complexity of the algorithm. Moreover, we derived a flexible weight function from the Welsch function to further improve the efficiency of our algorithm. To finally obtain accurate latent images, we developed a robust non-blind deblurring approach based on this flexible weight function. The experimental results indicate that our approach outperforms state-of-the-art methods in deblurring images with or without outliers. Compared with the method specifically for outliers, the recovery performance of our method can be improved by 12.9% (considering a dataset with impulse noise), and the execution efficiency is about 1.5 times faster.

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Data Availability

The data that support the findings of this study are available from the corresponding author on request.

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Acknowledgements

This work has been supported by National Natural Science Foundation of China (Grant No. 61971225), and National Science and Industry Bureau (Grant No. JCKY2016606B001). We would like to thank Editage (www.editage.cn) for English language editing.

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Authors and Affiliations

  1. School of Science, Nanjing University of Science and Technology, Nanjing, 210094, China

    Zhenhua Xu, Jiancheng Lai, Hongkun Huang & Zhenhua Li

  2. China Academy of Launch Vehicle Technology, Beijing, 100076, China

    Jun Zhou

  3. Faculty of Mathematics and Physics, Huaiyin Institute of Technology, Huai’an, 223001, China

    Huasong Chen

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  1. Zhenhua Xu
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  2. Jiancheng Lai
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  3. Jun Zhou
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Correspondence to Zhenhua Xu or Jiancheng Lai.

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Xu, Z., Lai, J., Zhou, J. et al. Image Deblurring Using a Robust Loss Function. Circuits Syst Signal Process 41, 1704–1734 (2022). https://doi.org/10.1007/s00034-021-01857-9

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