No abstract

Fluorescence microscopy plays an irreplaceable role in biomedicine. However, limited depth of field (DoF) of fluorescence microscopy is always an obstacle of image quality, especially when the sample is with an uneven surface or distributed in different depths. In this manuscript, we combine deep learning with Fresnel incoherent correlation holography to describe a method to obtain significant large DoF fluorescence microscopy. Firstly, the hologram is restored by the Auto-ASP method from out-of-focus to in-focus in double-spherical wave Fresnel incoherent correlation holography. Then, we use a generative adversarial network to eliminate the artifacts introduced by Auto-ASP and output the high-quality image as a result. We use fluorescent beads, USAF target and mouse brain as samples to demonstrate the large DoF of more than 400µm, which is 13 times better than that of traditional wide-field microscopy. Moreover, our method is with a simple structure, which can be easily combined with many existing fluorescence microscopic imaging technology.

Optical sectioning with high-throughput, a high signal-to-noise ratio (SNR), and submicrometer resolution is crucial, but challenging, to three-dimensional visualization of large biological tissue samples. Here we propose line-scanning imaging with digital structured modulation for optical sectioning. Our method generates images with a significantly improved SNR, compared to wide-field structured illumination microscopy (WF-SIM), without residual modulation artifacts. We image a 14.5 m m × 11.5 m m horizontal view of mouse brain tissue at a pixel resolution of 0.32 µ m × 0.32 µ m in 101 s, which, compared to WF-SIM, represents a significant improvement on imaging throughput. These results provide development opportunities for high-throughput, high-resolution large-area optical imaging methods.