KEYWORDS: Tissues, Objectives, Image resolution, Light sources and illumination, Microscopy, Visualization, Lenses, In vivo imaging, Point spread functions, Optical microsystems
We have developed a compact scattering-based light sheet microscopy (sLSM) probe capable of imaging unstained tissues with cellular resolution. In the compact sLSM probe, a custom miniature objective lens was developed to achieve a high lateral resolution, large field of view (FOV), and small field curvature. The measured resolution of the custom objective was 1.65 to 1.97 μm across the FOV of ±1.0 mm. The compact probe had dimensions of 4 cm in width and height and 10 cm in length. The compact sLSM probe achieved an axial resolution better than 5.6 μm over a depth range of 206.2 μm and a lateral resolution of 1.9 μm. Preliminary results showed that the compact sLSM probe could visualize cellular details of fixed human anal epithelial tissues in a similar manner to a bench light sheet microscopy device using off-the-shelf objective lenses.
In this paper, we demonstrate deep learning-based denoising of high-speed (180 fps) confocal images obtained with our low-cost SECM device. The CARE network was trained with 3090 high- and low-SNR image pairs on the Google Colab platform and tested with 45 unseen image pairs. The CARE prediction showed significant increase of SSIM and PSNR, and reduction of the banding noise while maintaining the cellular details. The preliminary results show the potential of using a deep learning-based denoising approach to enable high-speed SECM imaging.
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