  Non-mydriatic retinal color fundus photography (CFP) is widely available due
to the advantage of not requiring pupillary dilation, however, is prone to poor
quality due to operators, systemic imperfections, or patient-related causes.
Optimal retinal image quality is mandated for accurate medical diagnoses and
automated analyses. Herein, we leveraged the Optimal Transport (OT) theory to
propose an unpaired image-to-image translation scheme for mapping low-quality
retinal CFPs to high-quality counterparts. Furthermore, to improve the
flexibility, robustness, and applicability of our image enhancement pipeline in
the clinical practice, we generalized a state-of-the-art model-based image
reconstruction method, regularization by denoising, by plugging in priors
learned by our OT-guided image-to-image translation network. We named it as
regularization by enhancing (RE). We validated the integrated framework, OTRE,
on three publicly available retinal image datasets by assessing the quality
after enhancement and their performance on various downstream tasks, including
diabetic retinopathy grading, vessel segmentation, and diabetic lesion
segmentation. The experimental results demonstrated the superiority of our
proposed framework over some state-of-the-art unsupervised competitors and a
state-of-the-art supervised method.

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning