基于DenseNet的散射成像景深拓展研究

Expansion of Depth-of-Field of Scattering Imaging Based on DenseNet

散射现象广泛存在于自然界中。透过散射介质的大景深成像在计算成像领域具有重要的意义和应用价值。近年来,随着深度学习在计算成像领域的广泛应用,散射成像系统中的景深问题亟待进一步研究和拓展。以DenseNet为基础,结合UNet框架,建立了一个具有良好迁移性和景深拓展能力的深度卷积神经网络模型——DUNet。通过使用透过不同目数毛玻璃的散斑图像对网络进行训练,使成像景深拓展至距焦面50 mm处。初步的小鼠脑片实验结果表明,DUNet模型将有望应用于深层组织断层扫描。

Scattering is a fundamental phenomenon in nature.The imaging with large depth-of-field through a scattering medium is significant and valuable.In recent years,with the wide application of deep learning in computational imaging,it is urgent to study and further extend the depth-of-field in a scattering imaging system.In the paper,based on DenseNet and combined with the UNet architecture,a deep convolutional neural network model,namely DUNet,with good mobility and depth-of-field expansion ability is proposed.Moreover,the network is trained with speckle images passing through frosted glasses of different mesh,and the depth-of-field can be generalized to 50 mm away from the focal plane.The preliminary results on a rat brain slice demonstrate that the DUNet can be further implemented in the tomographic scanning of deep tissues.