Two-photon excitation fluorescence (2PEF) allows imaging of tissue up to
about one millimeter in thickness. Typically, reducing fluorescence excitation
exposure reduces the quality of the image. However, using deep learning super
resolution techniques, these low-resolution images can be converted to
high-resolution images. This work explores improving human tissue imaging by
applying deep learning to maximize image quality while reducing fluorescence
excitation exposure. We analyze two methods: a method based on U-Net, and a
patch-based regression method. Both methods are evaluated on a skin dataset and
an eye dataset. The eye dataset includes 1200 paired high power and low power
images of retinal organoids. The skin dataset contains multiple frames of each
sample of human skin. High-resolution images were formed by averaging 70 frames
for each sample and low-resolution images were formed by averaging the first 7
and 15 frames for each sample. The skin dataset includes 550 images for each of
the resolution levels. We track two measures of performance for the two
methods: mean squared error (MSE) and structural similarity index measure
(SSIM). For the eye dataset, the patches method achieves an average MSE of
27,611 compared to 146,855 for the U-Net method, and an average SSIM of 0.636
compared to 0.607 for the U-Net method. For the skin dataset, the patches
method achieves an average MSE of 3.768 compared to 4.032 for the U-Net method,
and an average SSIM of 0.824 compared to 0.783 for the U-Net method. Despite
better performance on image quality, the patches method is worse than the U-Net
method when comparing the speed of prediction, taking 303 seconds to predict
one image compared to less than one second for the U-Net method.
