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Fundus Image Quality Assessment and Enhancement: a Systematic Review

Heng Li, Haojin Li, Mingyang Ou, Xiangyang Yu, Xiaoqing Zhang, Ke Niu, Huazhu Fu, Jiang Liu

TL;DR

This systematic review addresses the problem of degraded fundus image quality and its impact on diagnosis by integrating analysis of image quality assessment (IQA) and enhancement (IQE) techniques. It provides a taxonomy of FR-IQA vs NR-IQA, and handcrafted vs learning-based IQE, alongside discussions of datasets, domain adaptation/generalization, and interpretability. Key contributions include a cohesive synthesis of algorithmic paradigms, practical deployment considerations, and guidance for future research toward clinical adoption, including medical-knowledge embedding and cross-modality generalization. The findings highlight data scarcity, annotation variability, and the need for interpretable, diagnostically aligned IQA/IQE methods to meaningfully improve downstream ophthalmic tasks and patient outcomes.

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.

Fundus Image Quality Assessment and Enhancement: a Systematic Review

TL;DR

This systematic review addresses the problem of degraded fundus image quality and its impact on diagnosis by integrating analysis of image quality assessment (IQA) and enhancement (IQE) techniques. It provides a taxonomy of FR-IQA vs NR-IQA, and handcrafted vs learning-based IQE, alongside discussions of datasets, domain adaptation/generalization, and interpretability. Key contributions include a cohesive synthesis of algorithmic paradigms, practical deployment considerations, and guidance for future research toward clinical adoption, including medical-knowledge embedding and cross-modality generalization. The findings highlight data scarcity, annotation variability, and the need for interpretable, diagnostically aligned IQA/IQE methods to meaningfully improve downstream ophthalmic tasks and patient outcomes.

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.
Paper Structure (37 sections, 17 equations, 16 figures, 4 tables)

This paper contains 37 sections, 17 equations, 16 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Fundus Imaging System and Interference
  3. Fundus IQA
  4. Full-reference IQA
  5. Numerical FR-IQA Metrics
  6. Learning-based FR-IQA Methods
  7. No-reference IQA
  8. Numerical NR-IQA metrics
  9. End-to-end NR-IQA methods
  10. Comprehensive NR-IQA Methods
  11. Challenges and Insight
  12. Fundus IQE
  13. Handcrafted IQE Methods
  14. Pixel-based Image Processing
  15. Image Filtering
  16. ...and 22 more sections

Figures (16)

  • Figure 1: Fundus photography in clinics. (a) Portable fundus photography, (b) fundus photography for infancy, (b) fundus photography for cataract patients.
  • Figure 2: The functions of IQA and IQE in guaranteeing high-quality fundus images. IQA assesses the perceptual quality of fundus images to determine their suitability for diagnosing diseases. A qualified image is directed towards diagnosis, while an unqualified one necessitates recapture. IQE enhances the perceptual quality of fundus images to facilitate their suitability for diagnostic purposes.
  • Figure 3: Manuscript taxonomy
  • Figure 4: Imaging system and interference of fundus photography.
  • Figure 5: Exhibition of numerical FR- and NR-IQA metrics on images affected by (b) darkening, (c) blurring, (d) spotting, and (e) displacement compared to the reference (a). Lower MSE scores, along with higher PSNR and SSIM scores indicate higher image quality.
  • ...and 11 more figures