DC-UMnet网络解全息欠采样包裹相位的方法  

DC-UMnet network method for unwrapping the phase of holographic undersampling

作　　者：张晶晶 李建素 党长营 陈颢文 杨钊 ZHANG Jingjing;LI Jiansu;DANG Changying;CHEN Haowen;YANG Zhao(College of Mechanical Engineering,North University of China,Taiyuan 030051,China)

机构地区：[1]中北大学机械工程学院,山西太原030051

出　　处：《红外与激光工程》2024年第10期260-270,共11页Infrared and Laser Engineering

基　　金：山西省基础研究计划资助项目(20210302123047,202103021224199)。

摘　　要：为了解决数字全息数值重构时,欠采样包裹相位的解包裹问题,提出了一种DC-UMnet网络解欠采样包裹相位的空间相位解包裹方法。DC-UMnet网络以U-net网络的解码器-编码器为框架,在编码部分融入Mobilenetv1的轻量级深度学习网络,以降低模型复杂程度、参数数量和运算成本;在解码部分提出了一种复杂的卷积模块-双通道模块(Dual-Channel Block)代替原来U-net网络中的3×3卷积,其在解码过程中更好地融合了特征,从而更好地解调欠采样包裹相位。同时使用SmoothL1Loss损失函数计算损失值,激活函数使用ReLU6,最后对欠采样模拟数据集进行了训练,并对实验获取的欠采样包裹相位图进行了实验验证。仿真结果表明,通过大量的模拟测试,验证了相比于DCT方法和U-net网络,DC-UMnet网络的结构性相似指数值分别提高了约4.38倍和0.77倍,在添加噪声后测试结果表明,DC-UMnet网络的结构性相似指数值分别提高了约4.68倍和0.86倍,适合应用到需要准确地进行欠采样相位解包裹中去;以欠采样微孔为对象的实验结果表明,文中方法获得微孔纵向尺寸的准确率为91.2%,验证了文中提出网络模型的可行性。该研究满足了欠采样包裹相位高质量解包裹的要求,为解决欠采样条件下的相位解包裹问题提供了新的思路方法。Objective:With the development of optical imaging technology,the advantages of digital holography have gradually emerged in the fields of surface contour inspection,industrial inspection,microscopic particle imaging and biomedical interferometry.Realizing high-precision phase unwrapping is the main key technology in the process of digital holography reconstruction.Phase unwrapping is to restore the phase information wrapped in the(−π,π]interval to the real phase information which changes continuously.Many phase unwrapping methods have been proposed in domestic and foreign related researches,including the research of algorithms and the application of deep learning techniques.But for the under-sampling problem due to the fast phase change,the classic phase unwrapping methods can solve the undersampling problem only within a certain range,and the difficulty lies in how to correctly recover the accurate phase distribution when the undersampling is more serious and the phase change is too fast.In order to solve the above problems,this paper proposes a spatial phase unwrapping method based on the DC-UMnet networks.Methods A large number of simulated datasets are used to establish mapping relationships between wrapped and unwrapped phases by means of supervised learning.To address the problem of undersampling,this paper proposes a spatial phase unwrapping method based on the DC-UMnet network to unwrap the undersampling wrapped phase.The DC-UMnet network utilizes the U-net network as the framework.In order to reduce the complexity of the model,the number of parameters,and the cost of computation,it is integrated into the lightweight deep learning network of Mobilenetv1 in the encoding part.And it is integrated into the decoding part by the Dual-Channel block.The Dual-Channel convolution mode used in the Dual-Channel block better fuses the extracted features,so that the demodulated undersampling parcels phase information will have a higher accuracy.Finally,the optimal loss function and activation function suitable for

关 键 词：数字全息 欠采样 DC-UMnet网络 相位解包裹 

分 类 号：O438.1[机械工程—光学工程]