RESToring Clarity: Unpaired Retina Image Enhancement using Scattering Transform

Abstract

Retina images are non-invasive and highly effective in diagnosis of various diseases such as cardiovascular and ophthalmological diseases. Accurate diagnosis de pends on the quality of the retina images, however, obtaining high-quality images can be challenging due to various factors, such as noise, experience level of the clinician, and eye movement of the patient. Methods for enhancing retina images are therefore in high demand for clinical purposes, yet the problem remains challenging as there is a natural trade-off between preserving anatomical details (e.g., vessels) and increasing overall image quality other than the content in it. Moreover, training an enhancement model often requires paired images that map low-quality images to high-quality images, which may not be available in practice. In this regime, we propose a novel Retina image Enhancement framework using Scattering Transform (REST). REST uses unpaired retina image sets and does not require prior knowledge of the degraded factors. The generator in REST enhances retina images through two branches with different roles: the Anatomy Preserving Branch (APB) and the Tone Transferring Branch (TTB). Our model can successfully enhance low-quality retina images demonstrating commendable results on two independent datasets, UKB and EyeQ.