  We leverage deep learning techniques to jointly denoise and super-resolve
biomedical images acquired with fluorescence microscopy. We develop a deep
learning algorithm based on the networks and method described in the recent W2S
paper to solve a joint denoising and super-resolution problem. Specifically, we
address the restoration of SIM images from widefield images. Our TensorFlow
model is trained on the W2S dataset of cell images and is made accessible
online in this repository: https://github.com/mchatton/w2s-tensorflow. On test
images, the model shows a visually-convincing denoising and increases the
resolution by a factor of two compared to the input image. For a 512 $\times$
512 image, the inference takes less than 1 second on a Titan X GPU and about 15
seconds on a common CPU. We further present the results of different variations
of losses used in training.
