Low signal to noise ratio (SNR) conditions degrade microscopy imaging quality, which complicates downstream post-processing and analysis. One conventional method to improve SNR by reducing noise is to average a large number of sequentially acquired images. However, this results in increased data acquisition time and reduced throughput. Longer exposures are also problematic for light-sensitive samples. We developed an alternative method, using a deep learning model based on the U-Net architecture that significantly reduces the number of images required to obtain exceptionally high SNR. Our model takes 5 noisy grayscale images as an input to generates a denoised image as an output. The model is trained on synthetically generated examples with added noise and fine tuned on real data. We demonstrate fast and robust denoising for images of fluorescent samples. Our method is capable of enhancing features while minimizing sample degradation from prolonged light exposure.
Conventional microscopy focusing methods perform a time consuming sweep through the Z-axis in order to estimate the focal plane. As an alternative, we developed a deep learning model that predicts in one shot the distance offset to the focal plane from any initial position using an input of only two images taken a set distance apart. The difference of these two images is processed through a regression CNN model, which was trained to learn a direct mapping between the amount of defocus aberration and the distance from the focal plane. A training dataset was acquired from a semiconductor sample at different surface locations on the sample and at different distances from focus. The ground truth focal plane was determined using a parabolic autofocus algorithm with the Tenengrad scoring metric. The CNN model was tested on bare semiconductor sample using the projected shape of the F-stop. The model was able to determine the in-focus position with high reliability, and was also significantly faster than conventional methods that rely on classical computer vision. Furthermore, the rare cases where our algorithm does not find the focal plane can be detected, and a fine-focus algorithm can be applied to correct the result. With the collection of sufficient training data, our deep learning focusing model provides a significantly faster alternative to conventional focusing methods.
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