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

作　　者：Pengwei Liang Junjun Jiang Xianming Liu Jiayi Ma 

机构地区：[1]School of Computer Science and Technology,Harbin Institute of Technology,Harbin 150001,China [2]Electronic Information School,Wuhan University,Wuhan 430072,China [3]IEEE

出　　处：《IEEE/CAA Journal of Automatica Sinica》2022年第5期878-892,共15页自动化学报（英文版）

基　　金：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);the Chinese Association for Artificial Intelligence(CAAI)-Huawei Mind Spore Open Fund

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 introdu-ced 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(Ba-MBNet),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.

关 键 词：Blur kernel convolutional neural networks(CNNs) defocus deblurring dual-pixel(DP)data META-LEARNING 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]