  Diabetic Retinopathy is the leading cause of blindness in the working-age
population of the world. The main aim of this paper is to improve the accuracy
of Diabetic Retinopathy detection by implementing a shadow removal and color
correction step as a preprocessing stage from eye fundus images. For this, we
rely on recent findings indicating that application of image dehazing on the
inverted intensity domain amounts to illumination compensation. Inspired by
this work, we propose a Shadow Removal Layer that allows us to learn the
pre-processing function for a particular task. We show that learning the
pre-processing function improves the performance of the network on the Diabetic
Retinopathy detection task.
