In this paper, we propose an advanced three-dimensional visualization method for an integral imaging microscope system to simultaneously improve the resolution and quality of the reconstructed image. The main advance of the proposed method is that it generates a high-quality three-dimensional model without limitation of resolution by combining the high-resolution two-dimensional color image with depth data obtained through a fully convolutional neural network. First, the high-resolution two-dimensional image and an elemental image array for a specimen are captured, and the orthographic-view image is reconstructed from the elemental image array. Then, via a convolutional neural network-based depth estimation after the brightness of input images are uniformed, a more accurate and improved depth image is generated; and the noise of result depth image is filtered. Subsequently, the estimated depth data is combined with the high-resolution two-dimensional image and transformed into a high-quality three-dimensional model. In the experiment, it was confirmed that the displayed high-quality three-dimensional model could be visualized very similarly to the original image.

中文翻译：

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使用全卷积深度估计网络的集成成像显微镜的高级三维可视化系统

在本文中，我们为整体成像显微镜系统提出了一种先进的三维可视化方法，以同时提高重建图像的分辨率和质量。该方法的主要进步在于，它通过将高分辨率二维彩色图像与通过全卷积神经网络获得的深度数据相结合，生成了不受分辨率限制的高质量三维模型。首先，采集高分辨率二维图像和样本的元素图像阵列，并从元素图像阵列重建正投影图像。然后，在输入图像的亮度均匀后，通过基于卷积神经网络的深度估计，生成更准确和改进的深度图像；并对结果深度图像的噪声进行过滤。随后，估计的深度数据与高分辨率二维图像结合，转化为高质量的三维模型。在实验中，证实所显示的高质量三维模型可以与原始图像非常相似地进行可视化。