Digital holographic phase unwrapping based on UMnet
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摘要: 为了实现高精度的数字全息相位解包裹, 采用基于Unet网络框架中集成MobilenetV3的轻量级深度学习网络, 设计了UMnet网络以实现全息相位的精准解包裹。网络中融和轻量级注意力机制、多尺度卷积来增强网络精度与泛化能力, 同时运用hard-Swish激活函数提高网络学习能力; 运用模拟数据集进行网络训练, 对生成网络模型进行降噪能力测试, 并经过了实际样品全息图的测试验证。结果表明, UMnet比深度学习相位解包裹网络的结构性相似指数值提升了6.6%。UMnet能够简单、快速、高效地实现数字全息相位解包裹。Abstract: In order to realize high-precision digital holographic phase unwrapping, a lightweight deep learning network based on MobilenetV3 integrated in the Unet network framework adopted, and the UMnet network designed to realize accurate unwrapping of holographic phase. The network lightweight attention mechanism and multi-scale convolution to enhance the network accuracy and generalization ability. At the same time, the hard-Swish activation function improve the network learning ability. The simulated data set used for network training, and the noise reduction ability of the generated network model tested, which verified by the test of the hologram of the actual sample. The results show that the structural similarity index of UMnet is 6.6% higher than that of deep learning phase unwrapping network. UMnet can realize digital holographic phase unwrapping simply, quickly and efficiently.
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Keywords:
- holography /
- phase unwrapping /
- attention mechanism /
- multi-scale convolution
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图 8 模拟测试集加入不同椒盐密度的椒盐噪声通过DCT-LS, TIE-FFT, DLPU, UMnet数字全息解包裹
Figure 8. Salt and pepper noise with different salt and pepper density is added to the analog test set and unpacked by DCT-LS, TIE-FFT, DLPU and UMnet digital holography
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图 9 加入噪声后不同方法相位解包裹SSIM值
Figure 9. Different methods of phase unwrapping SSIM value after adding noise
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图 10 血细胞、骨髓癌细胞真实相位图及3维展示
Figure 10. Real phase diagram and 3-D display of blood cells and bone marrow cancer cells
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图 11 血细胞加入不同椒盐密度噪声后相位解包裹
Figure 11. Phase unwrapping of blood cells after adding noise of different salt and pepper density
表 1 左侧收缩路径和桥接路径
Table 1 Left shrink path and bridge path
layer input operator output SE AFT stride 1 8 Conv 2d, 3×3 16 false hard-Swish 2 2 16 bneck, 3×3 16 false ReLU 1 2 16 bneck, 3×3 32 ture ReLU 2 3 32 bneck, 3×3 32 false ReLU 1 3 32 bneck, 5×5 64 ture ReLU 2 4 64 bneck, 5×5 64 false ReLU 1 4 64 bneck, 5×5 64 false ReLU 1 4 64 bneck, 3×3 128 ture hard-Swish 2 5 128 bneck, 3×3 128 false hard-Swish 1 5 128 bneck, 3×3 128 false hard-Swish 1 5 128 bneck, 3×3 128 false hard-Swish 1 5 128 bneck, 3×3 128 false hard-Swish 1 5 128 bneck, 3×3 128 false hard-Swish 1 5 128 bneck, 5×5 256 ture hard-Swish 2 6 256 bneck, 5×5 256 ture hard-Swish 1 6 256 bneck, 5×5 256 ture hard-Swish 1 6 256 Conv 2d, 1×1 256 false hard-Swish 1 
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