傅里叶叠层显微成像技术通过拓展频谱的方法合成细节信息更为丰富的单帧图像,实现在大视场下重建高分辨率图像。然而,成像系统中普遍存在的各种像差往往导致成像模糊,重建图像分辨率下降。针对上述问题,提出一种基于叠层衍射成像的像差校正方法,在更新频谱和光瞳函数时,通过自适应选取频谱和光瞳函数当前值与最大值的最佳比例,提高了迭代重建的质量。利用上述方法,首先重建加载混合像差的仿真图像,并选用峰值信噪比(PSNR)和结构相似性(SSIM)为评价指标。仿真结果表明,相比于传统的嵌入式光瞳恢复算法,本文方法可以大幅提升重建光瞳函数的PSNR和SSIM,分别增长14.9%和1.4%。为进一步验证算法在真实图像上的有效性,采集了人体血细胞样本图像并进行重建,结果表明,重建图像清晰,能够准确分辨细胞轮廓。

With the aid of Fourier ptychographic microscopy, we can synthesize single-frame images with more details after spectrum expansion, thus reconstructing high-resolution images in a large field of view. However, the ubiquitous aberrations in imaging systems often lead to blurry images and reduce resolution of the reconstructed images. To solve the above problems, we proposed an innovative aberration correction method based on ptychographical iterative engine. Specifically, when updating the spectrum and pupil function, we adaptively selected the optimal ratio between their current and maximum values to improve the quality of iterative reconstruction. Then, we reconstructed the simulated images with mixed aberrations by taking peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) as evaluation metrics. The simulation results showed that compared with the traditional embedded pupil recovery algorithm, our method sharply increased the PSNR and SSIM (14.9% and 1.4%, respectively). To further verify the effectiveness of our method on real images, we collected human blood cell samples for reconstruction. The reconstructed images were clear, which contributed to distinguishing the cell contours accurately.