  Deep neural networks for image quality enhancement typically need large
quantities of highly-curated training data comprising pairs of low-quality
images and their corresponding high-quality images. While high-quality image
acquisition is typically expensive and time-consuming, medium-quality images
are faster to acquire, at lower equipment costs, and available in larger
quantities. Thus, we propose a novel generative adversarial network (GAN) that
can leverage training data at multiple levels of quality (e.g., high and medium
quality) to improve performance while limiting costs of data curation. We apply
our mixed-supervision GAN to (i) super-resolve histopathology images and (ii)
enhance laparoscopy images by combining super-resolution and surgical smoke
removal. Results on large clinical and pre-clinical datasets show the benefits
of our mixed-supervision GAN over the state of the art.
