Enhancement of Retinal Fundus Images via Pixel Color Amplification

TL;DR

This paper introduces a pixel color amplification theory and new enhancement methods for retinal images, significantly improving segmentation performance and addressing data imbalance issues.

Contribution

A novel pixel color amplification theory and a family of retinal image enhancement methods, including a new derivation of Unsharp Masking, to improve segmentation tasks.

Findings

Large Dice score improvements in segmentation tasks

Effective for unbalanced and difficult data

Enhancement methods enable class balancing

Abstract

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.