BaMBNet: A Blur-Aware Multi-Branch Network for Dual-Pixel Defocus Deblurring
文献类型：期刊论文
| 作者 | Pengwei Liang; Junjun Jiang; Xianming Liu; Jiayi Ma |
| 刊名 | IEEE/CAA Journal of Automatica Sinica
 |
| 出版日期 | 2022 |
| 卷号 | 9期号:5页码:878-892 |
| ISSN号 | 2329-9266 |
| 关键词 | Blur kernel convolutional neural networks (CNNs) defocus deblurring dual-pixel (DP) data meta-learning |
| DOI | 10.1109/JAS.2022.105563 |
| 英文摘要 | 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. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/47551]  |
| 专题 | 自动化研究所_学术期刊_IEEE/CAA Journal of Automatica Sinica |
| 推荐引用方式 GB/T 7714 | Pengwei Liang,Junjun Jiang,Xianming Liu,et al. BaMBNet: A Blur-Aware Multi-Branch Network for Dual-Pixel Defocus Deblurring[J]. IEEE/CAA Journal of Automatica Sinica,2022,9(5):878-892. |
| APA | Pengwei Liang,Junjun Jiang,Xianming Liu,&Jiayi Ma.(2022).BaMBNet: A Blur-Aware Multi-Branch Network for Dual-Pixel Defocus Deblurring.IEEE/CAA Journal of Automatica Sinica,9(5),878-892. |
| MLA | Pengwei Liang,et al."BaMBNet: A Blur-Aware Multi-Branch Network for Dual-Pixel Defocus Deblurring".IEEE/CAA Journal of Automatica Sinica 9.5(2022):878-892. |
入库方式： OAI收割
来源：自动化研究所
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