ZEUS: Zernike based nEUral network for light Sheet microscopy

In recent years, deep learning has been used widely to solve a variety of digital microscopy problems. We present ZEUS as a method to correct out of focus aberrations and denoise light-sheet microscopy images. First, a convolutional neural network is used to estimate the aberrations in terms of Zernike coefficients. Then those values are used to train a UNET that outputs corrected images from noisy and aberrated ones. With this approach, we can access scanning frequencies and image qualities equivalent to the most advanced LSM systems without the need for costly equipment and complex optical setups.