A GAN-based deep enhancer for quality enhancement of retinal images photographed by a handheld fundus camera.

ABSTRACT:

Objective

Due to limited imaging conditions, the quality of fundus images is often unsatisfactory, especially for images photographed by handheld fundus cameras. Here, we have developed an automated method based on combining two mirror-symmetric generative adversarial networks (GANs) for image enhancement.

Methods

A total of 1047 retinal images were included. The raw images were enhanced by a GAN-based deep enhancer and another methods based on luminosity and contrast adjustment. All raw images and enhanced images were anonymously assessed and classified into 6 levels of quality classification by three experienced ophthalmologists. The quality classification and quality change of images were compared. In addition, image-detailed reading results for the number of dubiously pa

SUBMITTER: Fu J 

PROVIDER: S-EPMC10577846 | biostudies-literature | 2022 Nov-Dec

REPOSITORIES: biostudies-literature