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REVIEW 2 major objections 8 minor 126 references

Fundus Image Quality Assessment and Enhancement: a Systematic Review

T0 review · 2 major / 8 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read This review argues that assessing and enhancing fundus image quality should be treated as one problem, not two, and maps the algorithms, datasets, and deployment hurdles that follow.

desk verdict A useful integrated narrative review of fundus IQA and IQE, but the 'Systematic Review' label overpromises given no documented search protocol. read the letter →

arxiv 2501.11520 v1 pith:S5VLLPKS submitted 2025-01-20 eess.IV cs.CV

Heng Li , Haojin Li , Mingyang Ou , Xiangyang Yu , Xiaoqing Zhang , Ke Niu , Huazhu Fu , Jiang Liu This is my paper · ORCID
classification eess.IVcs.CV
keywords fundusphotographyimagequalityassessmentenhancementno-referencefull-referencedomainadaptationclinicaldeploymentsystematicreview
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper is a systematic review whose central claim is that fundus image quality assessment (IQA) and enhancement (IQE) are two halves of one clinical problem and should be reviewed together, not in separate surveys. The authors note that fundus photography captures clear critical structures in fewer than half of patients in some settings, and roughly a fifth of clinical images are unsuitable for automated diagnosis, which makes quality control a practical bottleneck for eye screening. They organise IQA into full-reference methods (which compare against an ideal image) and no-reference methods (which score quality without one), and IQE into handcrafted techniques versus learning-based models trained on paired or unpaired data. Across both tasks they identify shared datasets, shared metrics, and a common set of deployment obstacles—data scarcity, annotation inconsistency, domain shift, and interpretability—and argue that future progress lies in embedding medical knowledge, interpreting image interferences, decoupling visual from semantic content, and adapting continuously to each clinic.

What carries the argument

The organising device is a two-axis taxonomy: IQA methods are classified by whether they require a reference image (full-reference versus no-reference) and IQE methods by whether they are handcrafted or learning-based, with learning-based IQE further split into paired-data and unpaired-data training. Within this taxonomy the load-bearing technical objects are the numerical metrics (MSE, PSNR, SSIM and its variants; BRISQUE, NIQE, PIQE), the CycleGAN unpaired translation architecture with adversarial and cycle-consistency losses, and the imaging-prior models such as the dark channel prior adapted from dehazing to cataractous fundus images. These objects carry the argument because they show how assessment and enhancement share a common vocabulary: the same metrics used to score quality are used to evaluate enhancers, and the same degradation models used to synthesise training pairs for IQE are the distortions that IQA must detect. The taxonomy is what lets the review claim that IQA and IQE are one field rather than two.

What would settle it

A concrete check would be to run a documented, reproducible literature search (fixed databases, query strings, inclusion criteria) for fundus IQA and IQE papers and compare the resulting corpus with the tables in this review; if substantial clusters of published methods fall outside the taxonomy, or if the integrated-perspective premise fails because few papers actually combine IQA with IQE, the review’s central claims would need revision.

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Extended reading notes

Core claim

The central claim is that the field should be understood as a single pipeline with two cooperating functions. IQA determines whether a captured fundus image is usable; IQE restores images that are degraded but recoverable; together they decide the flow of accept, enhance, or recapture before diagnosis. The review systematises IQA by reference dependence (numerical and learning-based full-reference methods; numerical, end-to-end, and comprehensive no-reference methods) and IQE by technique origin (pixel processing, filtering, and statistical priors versus paired- and unpaired-data deep models), and it argues that the two literatures are converging: handcrafted priors such as the dark channel or green channel are being embedded into deep enhancers, quality sub-metrics are being predicted alongside overall grades for interpretability, and multi-task systems couple quality assessment with disease grading. On this view the main obstacles to clinical use are data availability, inconsistent quality labels across datasets, domain shift, and black-box behaviour, and the next advances will come from medical knowledge embedding, interference interpretation, information decoupling, personalisation, continual optimisation, and cross-modality generalisation.

Load-bearing premise

The review assumes that the papers it surveys, grouped under its full-reference versus no-reference and handcrafted versus learning-based taxonomy, are representative of the whole field, and that its qualitative claims about their strengths and weaknesses are accurate; because no reproducible search protocol is described, that coverage cannot be audited or independently reconstructed.

Editorial extensions

If this is right

  • Treating IQA and IQE as one pipeline suggests that future systems should be built as closed loops, where an assessment module decides between accept, enhance, or recapture before diagnosis.
  • Because both tasks share degradation models and datasets, progress in synthesizing realistic low-quality fundus images should improve both IQA training and IQE training at once.
  • The review implies that handcrafted priors (dark channel, green channel, structure) will increasingly be embedded inside learning-based models to gain interpretability without giving up performance.
  • Domain adaptation and domain generalization, not just benchmark accuracy, become the decisive criteria for whether an IQA and IQE method can be deployed in a new clinic.
  • Multi-task models that couple quality assessment with disease grading or vessel segmentation are a natural end point of the integrated view, since they let quality features act as inductive bias for diagnosis.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A testable extension the paper leaves implicit is an explicit ‘enhanceability’ label: instead of binary good or bad quality grades, datasets could carry a third label for images that are degraded but restorable, which would make the IQA–IQE loop directly trainable.
  • The integrated perspective suggests that evaluation protocols for IQE should be standardised around downstream clinical tasks (segmentation, grading) rather than only around perceptual metrics, since the review repeatedly ties quality to diagnostic utility.
  • One could check the review’s central claim empirically by measuring how often IQA and IQE methods are actually co-cited or jointly trained in the literature; if the two literatures barely overlap in practice, the ‘intimate relationship’ may be a design goal rather than a current fact.
  • The emphasis on source-free domain adaptation and continual learning implies that post-deployment data, normally discarded, could become the primary resource for keeping quality models aligned with each clinic’s imaging conditions.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 8 minor

Summary. This manuscript is a review of fundus image quality assessment (IQA) and enhancement (IQE). It first describes the fundus camera imaging system and common sources of image degradation, then categorizes IQA methods into full-reference and no-reference approaches, subdividing NR-IQA into numerical metrics, end-to-end learning-based methods, and comprehensive methods that introduce intermediate sub-metrics. For IQE, the paper distinguishes handcrafted methods (pixel-based processing, image filtering, statistical priors) from learning-based methods (paired and unpaired data paradigms) and further separates real-world from synthesized paired data. The latter half of the paper discusses practical deployment challenges such as data availability, generalizability, and interpretability, reviews applications of IQA and IQE in diagnosis and segmentation, and closes with six future research directions. The paper is written as a tutorial-style survey with equations for standard metrics and enhancement operations, tables of representative models and datasets, and example figures illustrating degradations.

Significance. If the manuscript's scope claims are taken at face value, it fills a gap by co-reviewing fundus IQA and IQE, which earlier surveys treat separately. The tutorial material is solid: standard formulas for MSE, PSNR, SSIM, histogram equalization, CLAHE, gamma correction, the dark-channel model, and CycleGAN losses are presented in a self-contained way, and the tables of representative IQA/IQE models and datasets provide a useful quick reference. The discussion of synthesized versus real-world paired data, domain adaptation/generalization, and the use of IQA to guide enhancement is timely and practically relevant. However, the contribution is currently weakened by two structural issues: the 'systematic review' claim is not backed by a reproducible search protocol, and the promised integrated perspective is not fully realized because the IQA and IQE halves are largely separate surveys with only limited cross-referencing. These issues are fixable within the manuscript's scope and are the main reasons for requesting major revision.

major comments (2)
  1. [Title, Abstract, Section I] The manuscript is presented as a 'Systematic Review' in the title and abstract, but no systematic methodology is described anywhere. There is no statement of the databases queried, the search strings used, the inclusion/exclusion criteria, the screening procedure, or the time window of coverage, and no PRISMA-style flow diagram or study-selection protocol. The reference list also contains a substantial number of the authors' own prior publications (e.g., [14], [23], [24], [34], [68], [91], [101], [102]), which increases the risk that the corpus is a convenience sample rather than an auditable comprehensive survey. Because the paper's central value proposition is that it provides a thorough, comprehensive, integrated review, the absence of a reproducible corpus is a load-bearing omission. The authors should either add a complete search and selection protocol, or explicitly reframe the manuscript as a narrative review in the title and abstract.
  2. [Sections III-V] The Introduction and Abstract promise an integrated perspective grounded in the 'intimate relationship' between IQA and IQE, but the body of the paper does not deliver this synthesis. Sections III and IV are two largely independent surveys, and the connections between the two fields appear only in isolated remarks, such as Hou et al. [90] using IQA to guide enhancement and the quality-feature fusion examples in Section V.B. The paper would substantially strengthen its central claim if it made the interplay explicit, for example by mapping the sub-metrics used in comprehensive NR-IQA (Section III.B.3) to the degradation targets addressed by IQE methods, or by discussing how the same datasets and quality labels support both tasks. Without such a conceptual bridge, the integrative contribution asserted in the Introduction is not fully demonstrated.
minor comments (8)
  1. [Fig. 1 caption] The caption repeats '(b)' for the second and third images; the third image should be labeled '(c)'.
  2. [Section III.B.3] 'Biaswas et al. [41]' is a misspelling of Biswas; later in the same paragraph 'NF-IQA' should be 'NR-IQA'.
  3. [Section IV.A.1, Eq. (7) text] The prose definition 'CFD(l) = sum_{l=0}^{L-1} H(l)/N' is not the cumulative distribution function; it should be 'CDF(l) = sum_{i=0}^{l} H(i)/N'. The actual computation in Eq. (7) uses the correct lower limit, so only the defining sentence needs correction.
  4. [Fig. 6 caption] 'Scematics' should be 'Schematics'.
  5. [Section IV.B] There are two typos: 'funds image data' should be 'fundus image data', and 'paried' should be 'paired'.
  6. [Table IV] The column headings 'Instance #' and 'Quality #' are ambiguous; the table would be clearer if the headers were 'Images' and 'Quality levels'. The superscript footnote markers for ODIR and iSee are also placed awkwardly in the dataset-name column.
  7. [References] References [83] and [103] appear to be the same paper (Guo et al., 'Bridging synthetic and real images: a transferable and multiple consistency aided fundus image enhancement framework'); one should be removed and the in-text citations renumbered and checked.
  8. [Table I] The 'Aspect' entry for SSIM is listed as 'Pixel-level and structure fidelity', but Eq. (3) defines SSIM in terms of luminance, contrast, and structure; consider revising to 'Luminance, contrast, and structure fidelity' for consistency.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the review is descriptive and self-contained, with self-citations used as examples rather than as load-bearing justification.

full rationale

This manuscript is a literature review, not a derivation. It makes no empirical predictions, fits no parameters, and proves no theorems from assumptions. Its central claim, that an integrated treatment of fundus IQA and IQE is more logical than separate reviews, is an organizational judgment supported by the cited literature rather than by a chain of reasoning whose conclusion is equivalent to its premises. The paper borrows definitions (e.g., MSE, PSNR, SSIM, BRISQUE, NIQE, PIQE), standard imaging models (e.g., Eqs. 11-12), and prior algorithmic results from the cited works, but this borrowing is the normal substance of a review, not circularity. Self-citations are present (e.g., refs. [14], [23], [24], [34], [68], [91], [101], [102]) and are used to illustrate paradigms in IQA/IQE and deployment; the review does not invoke a self-cited theorem to forbid alternatives or to force a conclusion, and the cited works themselves contain independent algorithmic contributions. Even if the absence of a reproducible search protocol weakens the auditable 'systematic' claim, that is a methodological limitation about external validity, not a logical circularity. No step in the paper reduces by construction to its own inputs, no fitted quantity is renamed as a prediction, and no ansatz is smuggled in via self-citation. Accordingly, the circularity score is 0.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

The review introduces no free parameters, new axioms beyond standard background assumptions, or invented entities. Its content consists of descriptions and classifications of already published work.

assumptions (2)
  • domain assumption Standard image quality metrics (MSE, PSNR, SSIM, NR-IQA metrics) are meaningful proxies for the clinical usefulness of fundus images.
    Invoked throughout Section III, where the review relies on these metrics to characterize distortions such as darkening, blurring, and spotting.
  • domain assumption The cited prior studies accurately describe their algorithms and reported performance.
    A review can only be as accurate as its sources; the paper does not independently re-implement or verify any cited method, so this assumption underlies every summary in Sections III and IV.

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Cite this review

Pith. "Pith review of Fundus Image Quality Assessment and Enhancement: a Systematic Review." pith.science (2026). https://pith.science/paper/S5VLLPKS

@misc{pith2026250111520,
  author       = {Pith},
  title        = {Pith review of: Fundus Image Quality Assessment and Enhancement: a Systematic Review},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/S5VLLPKS}},
  note         = {Machine review of arXiv:2501.11520}
}
read the original 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.

Figures

Figures reproduced from arXiv: 2501.11520 by the authors.

Figure 1
Figure 1. Fundus photography in clinics. (a) Portable fundus photography, (b) fundus photography for infancy, (b) fundus photography for cataract patients. image quality often degrades in complex conditions, leading to diagnostic uncertainty [5]. For instance, portable fundus photography ( [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
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 [PITH_FULL_IMAGE:figures/full… view at source ↗
Figure 4
Figure 4. Imaging system and interference of fundus photography. Following this, we examine the challenges and strategies for deploying IQA and IQE to boost fundus image understanding. Finally, we provide insights into potential future directions in this field. II. FUNDUS IMAGING SYSTEM AND INTERFERENCE Fundus photography, a non-invasive imaging method uti￾lized to capture intricate images of the eye’s posterior segment, is f… view at source ↗
Figures from the paper (12 more)
Figure 5
Figure 5. 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. A comparison of n…
Figure 5
Figure 5. Figure 5: Additionally, a list and value attributes of numerical [PITH_FULL_IMAGE:figures/full_fig_p004_5.png]
Figure 6
Figure 6. Figure 6: Scematics of naive end-to-end methods and comprehensive evaluation methods. Denote the model in end-to-end NR-IQA methods as Fθ, where θ is learnable parameters. Given training data {I, L}, where I represents target images and L denotes quality labels (either discrete …
Figure 7
Figure 7. Figure 7: Example histograms and images normalized by CLAHE and Gamma correction. (a) the raw image, (b) the image normalized by CLAHE, and (c) the image normalized by Gamma correction. processing, image filtering, and statistical prior techniques. On the other hand, learning-ba…
Figure 8
Figure 8. Figure 8: Image filter exhibition. (a) a noise image degraded by Gaussian noise, (b) the original high-quality image for reference, (c) Gaussian filter outcome, (d) Mean filter outcome, (e) Median filter outcome, and (d) Guided filter outcome. assigned to the filter kernel at po…
Figure 10
Figure 10. Figure 10: Learning-based IQE architectures using real-world and syn￾thesized paired data. capture. However, due to the uncontrolled relative eye motion in imaging, repeat captured images are susceptible to replace￾ment, resulting in pixel-level misalignments. Therefore, the col…
Figure 11
Figure 11. Figure 11: Learning-based IQE architectures using unpaired data. Ladv(GL→H, D) = E[log D(Rn)] + E[log(1 − D(GL→H(Ik)))], (16) where GL→H endeavors to translate Ik to a enhanced outcome GL→H(Ik), while D strives to distinguish between GL→H(Ik) and real high-quality samples Rn. No…
Figure 12
Figure 12. Figure 12: Capacity attributes of various training data types. On the other hand, the collection of training data poses a significant challenge in the advancement of fundus IQE methods. While the methods based on synthesized paired data and unpaired data circumvent the challenge…
Figure 13
Figure 13. Figure 13: Examples of variance in data distribution and annotation across fundus datasets. Table IV. These datasets are typically applicable to both IQA and IQE research. The attributes of these datasets, such as quality grading levels, collection conditions, and the presence o…
Figure 14
Figure 14. Figure 14: Illustration of ways to improve interpretability of IQA/IQE models. Interpretable fundus IQA and IQE algorithms assist oph￾thalmologists in comprehending the usability of fundus images and the reliability of enhancements. As depicted in [PITH_FULL_IMAGE:figures/full_…
Figure 15
Figure 15. Figure 15: Integrated workflow of fundus IQA and IQE into downstream tasks to guarantee image quality. IQA Application As summarized in Table IV, most previous datasets and re￾search categorize fundus images into two quality grades (high or low). Nevertheless, in clinical scenar…
Figure 16
Figure 16. Figure 16: Framework for integrating fundus IQA and IQE with down￾stream tasks. Approaches within this paradigm typically adopt a multi￾task learning framework, involving a branch dedicated to IQA or IQE alongside another branch for downstream tasks. As shown in [PITH_FULL_IMAG…

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.