BaMBNet: A Blur-Aware Multi-Branch Network for Dual-Pixel Defocus Deblurring

Pengwei Liang; Junjun Jiang; Xianming Liu; Jiayi Ma

doi:10.1109/JAS.2022.105563

Volume 9 Issue 5

May 2022

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2022 > 9(5): 878-892

P. W. Liang, J. J. Jiang, X. M. Liu, and J. Y. Ma, “BaMBNet: A blur-aware multi-branch network for dual-pixel defocus deblurring,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 5, pp. 878–892, May 2022. doi: 10.1109/JAS.2022.105563

Citation:

P. W. Liang, J. J. Jiang, X. M. Liu, and J. Y. Ma, “BaMBNet: A blur-aware multi-branch network for dual-pixel defocus deblurring,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 5, pp. 878–892, May 2022. doi: 10.1109/JAS.2022.105563

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BaMBNet: A Blur-Aware Multi-Branch Network for Dual-Pixel Defocus Deblurring

doi: 10.1109/JAS.2022.105563

1.
School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
2.
Electronic Information School, Wuhan University, Wuhan 430072, China

Funds: The research was supported by the National Natural Science Foundation of China (61971165, 61922027, 61773295), in part by the Fundamental Research Funds for the Central Universities (FRFCU5710050119), the Natural Science Foundation of Heilongjiang Province (YQ2020F004), and the Chinese Association for Artificial Intelligence (CAAI)-Huawei MindSpore Open Fund

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Author Bio:
Pengwei Liang received the B.S. degree from the School of Information Engineering, Wuhan University of Technology in 2017, and the M.S. degree from the Electronic Information School, Wuhan University in 2019. He is currently a Ph.D. candidate at the School of Computer Science and Technology, Harbin Institute of Technology. His research interests are in the areas of computer vision and pattern recognition

Junjun Jiang (Senior Member, IEEE) received the B.S. degree in mathematics from the Huaqiao University in 2009, and the Ph.D. degree in computer science from the Wuhan University in 2014. From 2015 to 2018, he was an Associate Professor with the School of Computer Science, China University of Geosciences. From 2016 to 2018, he was a Project Researcher with the National Institute of Informatics (NII), Japan. He is currently a Professor with the School of Computer Science and Technology, Harbin Institute of Technology. His research interests include image processing and computer vision

Xianming Liu (Member, IEEE) received the B.S., M.S., and Ph.D. degrees in computer science from the Harbin Institute of Technology (HIT) in 2006, 2008, and 2012, respectively. In 2011, he spent half a year at the Department of Electrical and Computer Engineering, McMaster University, Canada, as a visiting student, where he was a Post-Doctoral Fellow from 2012 to 2013. He was a Project Researcher with the National Institute of Informatics (NII), Japan, from 2014 to 2017. He is currently a Professor with the School of Computer Science and Technology, HIT. His research interests include image processing and computer vision. He was a recipient of the IEEE ICME 2016 Best Student Paper Award

Jiayi Ma (Senior Member, IEEE) received the B.S. degree in information and computing science and the Ph.D. degree in control science and engineering from the Huazhong University of Science and Technology in 2008 and 2014, respectively. He is currently a Professor with the Electronic Information School, Wuhan University. His research interests include computer vision, machine learning, and robotics. He has authored or co-authored more than 200 refereed journal and conference papers, including IEEE TPAMI/TIP, IJCV, CVPR, ICCV, ECCV, etc. He has been identified in the 2019−2021 Highly Cited Researcher lists from the Web of Science Group. He is an Area Editor of Information Fusion, an Associate Editor of Neurocomputing, Sensors and Entropy, and a Guest Editor of Remote Sensing
Corresponding author: Junjun Jiang, e-mail: jiangjunjun@hit.edu.cn
Received Date: 2022-01-24
Revised Date: 2022-02-15
Accepted Date: 2022-03-03

Available Online: 2022-03-20

Abstract

Abstract

Reducing the defocus blur that arises from the finite aperture size and short exposure time is an essential problem in computational photography. It is very challenging because the blur kernel is spatially varying and difficult to estimate by traditional methods. Due to its great breakthrough in low-level tasks, convolutional neural networks (CNNs) have been introduced to the defocus deblurring problem and achieved significant progress. However, previous methods apply the same learned kernel for different regions of the defocus blurred images, thus it is difficult to handle nonuniform blurred images. To this end, this study designs a novel blur-aware multi-branch network (BaMBNet), in which different regions are treated differentially. In particular, we estimate the blur amounts of different regions by the internal geometric constraint of the dual-pixel (DP) data, which measures the defocus disparity between the left and right views. Based on the assumption that different image regions with different blur amounts have different deblurring difficulties, we leverage different networks with different capacities to treat different image regions. Moreover, we introduce a meta-learning defocus mask generation algorithm to assign each pixel to a proper branch. In this way, we can expect to maintain the information of the clear regions well while recovering the missing details of the blurred regions. Both quantitative and qualitative experiments demonstrate that our BaMBNet outperforms the state-of-the-art (SOTA) methods. For the dual-pixel defocus deblurring (DPD)-blur dataset, the proposed BaMBNet achieves 1.20 dB gain over the previous SOTA method in term of peak signal-to-noise ratio (PSNR) and reduces learnable parameters by 85%. The details of the code and dataset are available at https://github.com/junjun-jiang/BaMBNet.
- Blur kernel,
- convolutional neural networks (CNNs),
- defocus deblurring,
- dual-pixel (DP) data,
- meta-learning

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References

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Instead of handling the entire image in a single network, BaMBNet assigns different regions with different blur amounts into multiple branches with different capacities, which can maintain the information of the clear regions while recovering the missing details of the blurred regions
we devise an unsupervised learn-based method to estimate the blur amounts of DP image, i.e. COC map
We use a novel assignment strategy to the estimated COC map to generate the defocus masks, which can effectively and efficiently guide the optimization of multi-branch network

BaMBNet: A Blur-Aware Multi-Branch Network for Dual-Pixel Defocus Deblurring

doi: 10.1109/JAS.2022.105563

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