Second harmonic generation (SHG) microscopy is expected to be a powerful tool for observing the cellular‐level functionality and morphology information of thick tissue owe to its unique imaging properties. However, the maximum attainable resolution obtainable by SHG microscopy is limited by the use of long‐wavelength, near‐infrared excitation. In this paper, we report the use of pixel reassignment to improve the spatial resolution of SHG microscopy. The SHG signal is imaged onto a position‐sensitive camera, instead of a point detector typically used in conventional SHG microscope. The data processing is performed through pixel reassignment and subsequent deblurring operation. We present the basic principle and a rigorous theoretical model for SHG microscopy using pixel reassignment (SHG‐PR). And for the first time, the optimal reassignment factor for SHG‐PR is derived based on the coherent characteristics and the dependence of wavelength in SHG microscopy. To evaluate the spatial resolution improvement, images of nano‐beads separated by different distances and of a microtubule array have been simulated. We gain about a 1.5‐fold spatial resolution enhancement compared to conventional SHG microscopy. When a further deblurring operation is implemented, this method allows for a total spatial resolution enhancement of about 1.87. Additionally, we demonstrate the validity of SHG‐PR for raw data with noise.

中文翻译：

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使用像素重新分配的二次谐波产生显微镜

由于其独特的成像特性，二次谐波发生（SHG）显微镜有望成为观察厚组织细胞水平功能和形态信息的有力工具。然而，SHG 显微镜可获得的最大分辨率受到使用长波长、近红外激发的限制。在本文中，我们报告了使用像素重新分配来提高 SHG 显微镜的空间分辨率。SHG 信号成像到位置敏感相机上，而不是传统 SHG 显微镜中通常使用的点检测器。数据处理通过像素重新分配和后续的去模糊操作进行。我们提出了使用像素重新分配 (SHG-PR) 的 SHG 显微镜的基本原理和严格的理论模型。第一次，SHG-PR 的最佳重新分配因子是基于 SHG 显微镜中的相干特性和波长依赖性得出的。为了评估空间分辨率的提高，模拟了不同距离分隔的纳米珠和微管阵列的图像。与传统的 SHG 显微镜相比，我们获得了大约 1.5 倍的空间分辨率增强。当实施进一步的去模糊操作时，该方法允许大约 1.87 的总空间分辨率增强。此外，我们证明了 SHG-PR 对带有噪声的原始数据的有效性。与传统的 SHG 显微镜相比，我们获得了大约 1.5 倍的空间分辨率增强。当实施进一步的去模糊操作时，该方法允许大约 1.87 的总空间分辨率增强。此外，我们证明了 SHG-PR 对带有噪声的原始数据的有效性。与传统的 SHG 显微镜相比，我们获得了大约 1.5 倍的空间分辨率增强。当实施进一步的去模糊操作时，该方法允许大约 1.87 的总空间分辨率增强。此外，我们证明了 SHG-PR 对带有噪声的原始数据的有效性。