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Volume 9 Issue 5
May  2022

IEEE/CAA Journal of Automatica Sinica

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Article Contents
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

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

doi: 10.1109/JAS.2022.105563
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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  • 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.

     

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    Highlights

    • 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

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