  We propose a pixel color amplification theory and family of enhancement
methods to facilitate segmentation tasks on retinal images. Our novel
re-interpretation of the image distortion model underlying dehazing theory
shows how three existing priors commonly used by the dehazing community and a
novel fourth prior are related. We utilize the theory to develop a family of
enhancement methods for retinal images, including novel methods for whole image
brightening and darkening. We show a novel derivation of the Unsharp Masking
algorithm. We evaluate the enhancement methods as a pre-processing step to a
challenging multi-task segmentation problem and show large increases in
performance on all tasks, with Dice score increases over a no-enhancement
baseline by as much as 0.491. We provide evidence that our enhancement
preprocessing is useful for unbalanced and difficult data. We show that the
enhancements can perform class balancing by composing them together.
