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Abstract：
Fourier Ptychographic Microscopy (FPM) is a microscopy imaging technique based on optical principles. It employs Fourier optics to separate and combine different optical information from a sample. However, noise introduced during the imaging process often results in poor resolution of the reconstructed image. This article has designed an approach based on a residual local mixture network to improve the quality of Fourier ptychographic reconstruction images. By incorporating channel attention and spatial attention into the FPM reconstruction process, the network enhances the efficiency of the network reconstruction and reduces the reconstruction time. Additionally, the introduction of the Gaussian diffusion model further reduces coherent artifacts and improves image reconstruction quality. Comparative experimental results indicate that this network achieves better reconstruction quality, and outperforming existing methods in both subjective observation and objective quantitative evaluation.
摘要：
傅里叶重叠显微术(FPM)是一种基于光学原理的显微成像技术。它采用傅立叶光学来分离和组合来自样品的不同光学信息。然而,在成像过程中引入的噪声往往导致重建图像的分辨率较差。本文设计了一种基于残差局部混合网络的方法来提高傅立叶重叠重建图像的质量。通过将通道注意力和空间注意力纳入FPM重建过程，提高了网络重构的效率,减少了重构时间。此外,高斯扩散模型的引入进一步减少了相干伪影,提高了图像重建质量。对比实验结果表明,该网络具有较好的重建质量,在主观观察和客观定量评价方面都优于现有方法。
