Thursday 27 March 2025
A comprehensive benchmark has been developed to evaluate the quality of retinal image enhancement models, a crucial step in the diagnosis and treatment of eye diseases. The benchmark, called EyeBench, assesses both the enhancement task itself as well as downstream tasks such as vessel segmentation and disease grading.
Retinal image enhancement is a complex challenge that requires careful evaluation to ensure that models are accurate and reliable. Previous attempts at evaluating these models have been limited by their focus on a single aspect of performance, such as the quality of the enhanced images or the ability to denoise real-world noise. EyeBench takes a more holistic approach, considering multiple aspects of performance simultaneously.
The benchmark consists of three main components: full-reference quality assessment, no-reference quality assessment, and downstream tasks. The full-reference assessment evaluates the quality of the enhanced images by comparing them directly to high-quality reference images. This component is particularly useful for assessing the ability of models to preserve fine details and textures in the images.
The no-reference assessment, on the other hand, evaluates the ability of models to denoise real-world noise without having access to high-quality reference images. This component is essential for evaluating the robustness of models to varying levels of noise and distortion.
In addition to these two components, EyeBench also includes a range of downstream tasks that assess the practical utility of the enhanced images. These tasks include vessel segmentation, disease grading, and lesion segmentation, which are all critical steps in the diagnosis and treatment of eye diseases.
The results of the benchmark demonstrate the importance of considering multiple aspects of performance when evaluating retinal image enhancement models. The best-performing models were those that achieved high scores across all components of the benchmark, indicating their ability to produce high-quality enhanced images while also preserving fine details and textures.
One of the key benefits of EyeBench is its ability to provide a comprehensive evaluation of retinal image enhancement models. By considering multiple aspects of performance simultaneously, researchers can gain a more complete understanding of the strengths and weaknesses of each model. This will ultimately lead to the development of more accurate and reliable models that are better suited to real-world clinical applications.
EyeBench is an important step forward in the evaluation of retinal image enhancement models, and its results have far-reaching implications for the diagnosis and treatment of eye diseases. As researchers continue to develop new models, EyeBench will provide a valuable tool for evaluating their performance and ensuring that they meet the high standards required in clinical practice.
Cite this article: “EyeBench: A Comprehensive Benchmark for Evaluating Retinal Image Enhancement Models”, The Science Archive, 2025.
Retinal Image Enhancement, Eye Diseases, Benchmark, Quality Assessment, Image Denoising, Vessel Segmentation, Disease Grading, Lesion Segmentation, Medical Imaging, Computer Vision
