Fundus Image Quality Assessment and Enhancement: a Systematic Review

2025-01-20

AI Summary

Fundus photography in clinical settings is susceptible to degradation from illumination variations, motion artifacts, and device-specific limitations, compromising image quality and diagnostic reliability. This paper presents a systematic review of fundus image quality assessment (IQA) and enhancement (IQE) methods published between 2010 and 2024. It is the first to explicitly characterize the synergistic interplay between IQA and IQE. We propose a unified taxonomy encompassing traditional algorithms, CNN- and Transformer-based models, GANs and diffusion models, as well as no-reference and full-reference quality metrics. Three critical evaluation dimensions—reproducibility, annotation robustness, and edge-deployment adaptability—are introduced. We further conduct an in-depth analysis of clinical deployment bottlenecks and outline practical optimization pathways for real-world applications. This work fills a significant gap by providing the first comprehensive, integrated survey of IQA and IQE, offering both theoretical foundations and actionable guidance for algorithm development and clinical translation.

Technology Category

• Computer Vision: Other Foundations of Computer Vision
• Machine Learning: Calibration & Uncertainty Quantification
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

Abstract

As an affordable and convenient eye scan, fundus photography holds the potential for preventing vision impairment, especially in resource-limited regions. However, fundus image degradation is common under intricate imaging environments, impacting following diagnosis and treatment. Consequently, image quality assessment (IQA) and enhancement (IQE) are essential for ensuring the clinical value and reliability of fundus images. While existing reviews offer some overview of this field, a comprehensive analysis of the interplay between IQA and IQE, along with their clinical deployment challenges, is lacking. This paper addresses this gap by providing a thorough review of fundus IQA and IQE algorithms, research advancements, and practical applications. We outline the fundamentals of the fundus photography imaging system and the associated interferences, and then systematically summarize the paradigms in fundus IQA and IQE. Furthermore, we discuss the practical challenges and solutions in deploying IQA and IQE, as well as offer insights into potential future research directions.

Problem

• Ocular Image Quality
• Complex Photography Conditions
• Medical Diagnostic Accuracy

Innovation

• Ophthalmic Image Enhancement
• Quality Assessment
• Medical Imaging Applications