Unified Framework for Enhancement of Low-Quality Fundus Images.

Compared to desktop fundus cameras, handheld ones offer portability and affordability, although they often produce lower-quality images. This paper primarily addresses the issue of reduced image quality commonly associated with images captured by handheld fundus cameras. We first collected 538 fundus images obtained from handheld devices to form a dataset called Mule. A unified framework that consists of three main modules is then proposed to enhance the quality of fundus images. The Light Balance Module is employed first to suppress overexposure and underexposure. This is followed by the Super Resolution Module to enhance vascular details. Finally, the Vessel Enhancement Module is applied to improve image contrast. And a special preservation strategy is additionally applied to retain mocular features in the final fundus image. Objective evaluations demonstrate that the proposed framework yields the most promising results. Further experiments also suggest that it improves accuracy in downstream tasks, such as vessel segmentation, optic disc/optic cup detection, macula detection, and fundus image quality assessment. Our code is available at: https://github.com/Alen880/UFELQ.