Ding Lihua, Zhang Chengyi, Lyu Xingzheng, Cheng Deji, Xu Shuchang
School of Information Science and Technology, HangZhou Normal University, Hangzhou, 311100, Zhejiang, China.
Hangzhou Mocular Medical Technology Inc., Lin'an District Future Eye Valley, Hangzhou, 311100, Zhejiang, China.
J Imaging Inform Med. 2025 Apr 29. doi: 10.1007/s10278-025-01509-3.
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.
与台式眼底相机相比，手持式相机具有便携性和经济性，尽管它们通常会产生质量较低的图像。本文主要解决与手持式眼底相机拍摄的图像相关的图像质量下降问题。我们首先收集了从手持设备获得的538张眼底图像，以形成一个名为Mule的数据集。然后提出了一个由三个主要模块组成的统一框架，以提高眼底图像的质量。首先使用光平衡模块来抑制过度曝光和曝光不足。接下来是超分辨率模块，以增强血管细节。最后，应用血管增强模块来改善图像对比度。此外，还应用了一种特殊的保留策略，以在最终的眼底图像中保留黄斑特征。客观评估表明，所提出的框架产生了最有前景的结果。进一步的实验还表明，它提高了下游任务的准确性，如血管分割、视盘/视杯检测、黄斑检测和眼底图像质量评估。我们的代码可在以下网址获取：https://github.com/Alen880/UFELQ 。
