Traditional fluorescence microscopy with conventional optics suffers from the trade-off between the resolution, field-of-view (FOV) and miniaturization. Computational imaging techniques overcome these limitations by leveraging miniature optics and enabling strong multiplexing. However, the shift-variant degradation caused by miniaturized lenses poses computational and memory challenges. In this work, we developed a Multi-channel FourierNet that learns the global shift variant filters in the frequency domain without any prior knowledge, providing consistent performance on a large-scale FOV. Additionally, we validate the effectiveness of our network by visualizing the correspondence between the saliency map and the truncated PSFs from different viewpoints. We demonstrate the network fueled by simulation data can perform real-time reconstruction on biological samples. We believe this innovative approach holds great promise for advancing computational imaging techniques across diverse applications.
HiLo microscopy is a widefield optical sectioning technique that involves computational reconstruction from two images, one with structured illumination and the other with uniform illumination. A variety of methods, including speckle and periodic grids, can be employed to achieve structured illumination. In this study, we introduce a novel HiLo strategy that utilizes an off-the-shelf holographic diffuser and a low-coherence LED source to generate random caustic patterns. This method offers several benefits over existing ones, such as simplicity and cost-effectiveness. We achieve 4.5 µm optical sectioning capability with a 20x 0.75 NA objective and demonstrate the performance of our method by imaging a 400 µm thick, highly scattering brain section. We anticipate that our caustic-based structured illumination approach will augment the versatility of HiLo microscopy and extend to various imaging applications.
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