Appl Opt. 2020 Jan 10;59(2):333-345. doi: 10.1364/AO.59.000333.
Digital holographic microscopy is becoming increasingly useful for the analysis of marine plankton. In this study, we investigate autofocusing and image fusion in digital holographic microscopy. We propose an area metric autofocusing method and an improved wavelet-based image fusion method. In the area metric autofocusing method, a hologram image is initially segmented into several plankton regions for focus plane detection, and an area metric is then applied to these regions. In the improved wavelet-based image fusion method, a marked map is introduced for labeling each plankton region with the order of refocus plane images that accounts for the most pixels. The results indicate that the area metric autofocusing method applied to each plankton region provides a higher depth resolution accuracy than a number of general autofocusing methods, and the mean accuracy increases by approximately 33%. The improved wavelet-based image fusion method can fuse more than nine reconstructed plane images at a time and effectively eliminate fringes and speckle noise, and the fused image is much clearer than that of a general wavelet-based method, a sparse decomposition method, and a pulse-coupled neural networks method. This work has practical value for plankton imaging using digital holographic microscopy.
数字全息显微镜在海洋浮游生物分析中变得越来越有用。在这项研究中，我们研究了数字全息显微镜中的自动对焦和图像融合。我们提出了一种区域度量自动对焦方法和一种改进的基于小波的图像融合方法。在区域度量自动对焦方法中，首先将全息图图像分割成几个浮游生物区域，以检测焦点平面，然后应用区域度量。在改进的基于小波的图像融合方法中，引入了标记图来标记每个浮游生物区域，标记图根据包含最多像素的重新聚焦平面图像的顺序对每个浮游生物区域进行标记。结果表明，应用于每个浮游生物区域的区域度量自动对焦方法比许多一般的自动对焦方法提供了更高的深度分辨率精度，平均精度提高了约 33%。改进的基于小波的图像融合方法可以一次融合超过九个重构的平面图像，并有效地消除条纹和散斑噪声，融合后的图像比一般的基于小波的方法、稀疏分解方法和脉冲耦合神经网络方法清晰得多。这项工作对于使用数字全息显微镜进行浮游生物成像具有实际价值。
