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REVIEW 3 major objections 4 minor 74 references

Unveiling the Underwater World: CLIP Perception Model-Guided Underwater Image Enhancement

T0 review · 3 major / 4 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read Adding a CLIP perception model trained on human opinion scores as a loss and curriculum regularizer improves underwater enhancement quality and generalization over state-of-the-art methods.

desk verdict Solid but modest plug-and-play CLIP losses improve UIE networks on full-reference metrics; the paper's generalization claim is contradicted by its own no-reference numbers. read the letter →

arxiv 2507.06234 v1 pith:5PEDV66X submitted 2025-04-29 cs.CV

Jiangzhong Cao , Zekai Zeng , Xu Zhang , Huan Zhang , Chunling Fan , Gangyi Jiang , Weisi Lin This is my paper · ORCID
classification cs.CV
keywords underwaterimageenhancementCLIPperceptionlosscurriculumcontrastiveregularizationpromptlearningqualityassessmentno-referenceevaluationvision-languagemodels
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

The paper argues that underwater image enhancement can be substantially improved by injecting human-perceptual judgment into the training loop through a CLIP vision-language model. It trains a small CLIP-based perception model—a softmax over cosine similarities between the image and two learnable prompts, 'Clear Underwater photo.' and 'Turbid Underwater photo.'—against mean opinion scores, then uses that model twice: as a perception loss that pushes enhanced images to look better than the reference, and as a scoring criterion that divides degraded 'negative' images into hard and very-hard classes for a curriculum contrastive regularizer. On the U90 test set the resulting network exceeds the NU2Net baseline it is built on (PSNR 23.115 vs 22.669, SSIM 0.929 vs 0.924, LPIPS 0.133 vs 0.154). The authors further claim the gains transfer to no-reference datasets U45, SQUID, and C60, and that the two loss modules can be added to other UIE networks plug-and-play without changing their structure. A careful reader should care because the method targets the widely noted perceptual shortcomings of existing UIE networks, not just pixel-level accuracy.

What carries the argument

The load-bearing object is the CLIP perception model: a frozen CLIP image encoder and text encoder with a learnable antonymic prompt pair (tokens T_p and T_n, both in $R^{{Nx512}}$), whose softmax of cosine similarities S_out = $e^{{S_p}}$/($e^{{S_p}}$+$e^{{S_n}}$) yields a predicted quality score in [0,1]. It is trained by L2 regression to human MOS labels on UEQAB (PLCC 0.83, SROCC 0.80 on the test set). Once trained, the prompt embeddings are frozen, and the model serves two roles: (a) it defines L_clip, a margin-style loss that drives the enhanced image's score above $\alpha$ times the reference's deficiency; and (b) it evaluates each of the six non-easy negatives N_q at each epoch, classifying each as 'hard' (if S_out^Y' > S_out^Nq, weight 1+gamma) or 'very-hard' (otherwise, weight 1-gamma), with gamma=0.25. The L_CR term then computes a weighted ratio of VGG-19 feature L1 distances between positive/negative/anchor, pulling the anchor to the reference and pushing it from a weighted combination of degraded images.

What would settle it

Train the same NU2Net-based UIE network with L1 + L_clip + L_CR but replace the CLIP score used to classify hard/very-hard negatives with random labels (or with the negative index order), keeping everything else identical. If the PSNR/SSIM/LPIPS results on U90 do not degrade materially compared to the proposed weighting, then the CLIP-based curriculum, not the mere presence of extra negatives, is not the source of the gains. Additionally, run a human preference study on 50 randomly sampled enhanced images from U45 and SQUID comparing the proposed method against NU2Net; if human raters do not choose the proposed method more often, the central perceptual-quality claim lacks support.

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Extended reading notes

Core claim

The central discovery is that a CLIP perception model trained on human opinions can act as a differentiable quality oracle for underwater images, and that using it simultaneously as a loss and as a curriculum weighting of negatives yields better enhancement than the same network trained with L1 alone or with earlier quality-assessment losses. Concretely, the paper's L_clip = max(0, (1-S_out(Y')) - alpha(1-S_out(Y))) with alpha=0.975 encourages the enhanced image Y' to achieve a CLIP score exceeding the reference, while L_CR contrasts VGG-19 features of the anchor Y' against the reference Y, the 6 non-easy negatives from fixed UIE methods, and the input; the weight of each non-easy negative is 1+gamma or 1-gamma depending on whether its CLIP score is below or above the current anchor's, making difficult negatives gradually easier over training. The ablation tables show each module helps, and that the CLIP-based version of L_CR outperforms the PSNR-based version, which is the load-bearing evidence for using CLIP as the quality criterion.

Load-bearing premise

The CLIP perception model trained on UEQAB mean opinion scores accurately judges perceptual quality on all the other test sets (U45, SQUID, C60), so that both the L_clip loss and the weighting of negatives in L_CR are guided by a trustworthy human-aligned quality score.

Editorial extensions

If this is right

  • Any existing UIE network (WaterNet, FUnIE, Shallow-UWnet, PUGAN, NU2Net) gains PSNR and SSIM when L_clip and L_CR are added, so the two loss modules are architecture-agnostic additions.
  • The CLIP perception model can serve as a no-reference evaluation score for underwater images, complementing UCIQE and UIQM, since it matches human MOS more closely on UEQAB.
  • Training with content-matched negatives at varying difficulty levels constrains the solution space and prevents both under- and over-enhancement, as shown by the ablation with single-negative and nine-negative configurations.
  • The method generalizes to no-reference test sets with diverse distortion types (color casts, low contrast, blur) without retraining on those sets.
  • Because L_clip is differentiable, the same perception model could be reused for other restoration tasks that optimize human-perceived quality.

Reading between the lines

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

  • Since the paper validates the CLIP perception model's correlation with human scores only on UEQAB, a natural test is to collect human MOS on the U45, SQUID, and C60 enhancement outputs; if CLIP scores disagree with human rankings there, the reported no-reference CLIP-Score improvements should be interpreted as model preference rather than human-perceived quality.
  • The binary hard/very-hard split with fixed gamma could be replaced by a continuous weight based on the CLIP score difference, which might yield smoother curriculum dynamics and faster convergence.
  • The six negative-generation methods are fixed and heuristic; using the CLIP perception model to also select or generate negatives (e.g., through learned degradation models) could strengthen the regularization and push the gain further.
  • The plug-and-play claim suggests a drop-in use for other underwater tasks like depth estimation or saliency detection, a direction the paper gestures at only through the SVAM-Net saliency experiment.
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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

3 major / 4 minor

Summary. The paper proposes a plug-and-play perceptual training scheme for underwater image enhancement (UIE). The authors first train a CLIP-based perception model on the UEQAB MOS dataset using learnable antonymic prompt pairs (Section III.B, Eqs. 1-4). This model is then used in two ways inside the enhancement network: as a CLIP perception loss Lclip (Eq. 5) and as a difficulty-weighting criterion for curriculum contrastive regularization LCR (Eqs. 6-7), where negatives come from fixed UIE methods (UDCP, IBLA, DCP, HE, FUnIE, USUIR). The enhancement backbone is NU2Net, and the method is also applied to WaterNet, FUnIE, Shallow-UWnet, and PUGAN. Full-reference evaluation on U90 reports PSNR 23.115, SSIM 0.929, LPIPS 0.133 versus NU2Net's 22.669, 0.924, 0.154. No-reference evaluation is reported on U45, SQUID, and C60 using UCIQE, UIQM, and a CLIP-Score, plus qualitative comparisons and a saliency-detection application test.

Significance. If the central claim is read narrowly, the paper provides a consistent ablation story: adding Lclip and LCR to NU2Net improves full-reference metrics on U90 (Table IV), and adding both losses to four other UIE backbones improves them as well (Table VIII). The CLIP perception model itself achieves competitive PLCC/SROCC on the UEQAB test set (Table I), and the application test with a saliency detector is a useful auxiliary demonstration. The main weaknesses are that the headline full-reference gains are small and are reported without any variance or significance analysis, and the no-reference evaluation is partly circular because the reported CLIP-Score is computed by the same model that is used as the optimization target and as the curriculum-weighting criterion. The abstract's generalization claim is not supported by Table III, which shows the proposed method below NU2Net on several no-reference metrics. These issues are fixable by narrowing claims, adding multi-seed statistics, and validating the CLIP model on the no-reference datasets, so the work is potentially publishable but needs substantive revision.

major comments (3)
  1. [Section IV.A.3 and Table III] Reporting CLIP-Score as a no-reference evaluation metric is circular for this method. The CLIP perception model that computes CLIP-Score is the same model optimized by Lclip (Eq. 5) and used to set the curriculum weights in Eq. (6), and it is validated only on UEQAB in Table I; Section IV.J itself concedes that the model "may benefit from a more accurate selection of initialization prompts." Moreover, Table III does not show consistent superiority on the no-reference datasets: the proposed method is below NU2Net in CLIP-Score on U45 (56.02 vs 56.89) and C60 (50.41 vs 50.81), and below NU2Net in UIQM on SQUID (2.360 vs 2.480) and C60 (2.810 vs 2.900). Therefore the abstract's claim that the method "outperforms state-of-the-art methods in terms of visual quality and generalization ability" is not supported. The authors should either add human MOS studies on U45/SQUID/C60, report an external no-reference metric that is not part of the training objective, or restrict the generalization claim to the full-reference U90 result.
  2. [Tables II, IV, and VIII] The full-reference gains on U90 are small (PSNR +0.446 dB, SSIM +0.005, LPIPS -0.021 relative to NU2Net), and every number in the paper appears to come from a single training run. No standard deviation, confidence interval, paired per-image test, or training-seed information is reported anywhere, even though the network structure is identical to NU2Net and gains of a few tenths of a dB could plausibly arise from run-to-run variation. Please report mean and standard deviation over multiple seeds and, where possible, paired per-image differences on U90 to establish that the proposed loss terms are the cause of the observed improvement.
  3. [Section IV.A.1] The SQUID test protocol is underspecified: the text says "we select 16 representative examples as the test set, which is the same as [16]," but gives no criterion for selecting these 16 examples. Because the generalization claim depends on the no-reference datasets U45, SQUID, and C60, the selection procedure must be described in advance or the full SQUID set should be used; otherwise the result cannot be distinguished from favorable subset selection.
minor comments (4)
  1. [Section IV.E] The text is internally inconsistent: it states "the scheme L1 + Lclip actually means NU2Net method [20]," but Table V shows that NU2Net's reported performance corresponds to L1 + LUranker (22.669), while L1 + Lclip gives 22.677 in the same table. The paragraph also names the best scheme as "L1 + LUranker" twice, with one instance presumably meant to be L1 + LBFEN. Please rewrite this paragraph and reconcile it with Table V.
  2. [Section IV.C] The text refers to "The SUIQD dataset" in the discussion of the no-reference datasets; this appears to be a typo for SQUID and should be corrected.
  3. [Algorithm 2 and Section IV.F] There is a discrepancy in the number of negatives: Algorithm 2 sets z=6 non-easy negatives and Section IV.A.2 lists six fixed UIE methods, but Section IV.F says "the number of negatives is set as 7" and labels the scheme "+CR(1:7)." Please clarify whether z excludes or includes the easy negative and align the notation.
  4. [Table III caption] The caption states that the top three results are marked with red, blue, and green, but the text does not explain or discuss these color markers, and the printed table does not make the ranking visually clear. Either explain the color coding in the text or remove it.

Circularity Check

1 steps flagged · score 4.0 of 10

Full-reference improvements are independently supported, but the non-reference CLIP-Score evaluation reuses the model that the training loss explicitly optimizes, giving partial circularity.

  1. fitted input called prediction [Section IV.A.3 (Evaluation metrics); Section III.C Eq.(5); Section III.D Eq.(6); Table III]
    "For the test datasets C60, U45, and SQUID, which do not contain reference images, ..., the proposed CLIP perception model is also employed for its stronger linear correlation with human visual perception. ... Lclip = max(0, ((1−SY′out)−α(1−SYout))). ... CLIP-Score↑"

    The CLIP-Score column in Table III is the output Sout of Eq.(3), produced by prompts fit to UEQAB MOS labels via Eq.(4). During enhancement training, Eq.(5) explicitly drives the enhanced image's Sout upward (α=0.975), and Eq.(6) sets the curriculum-negative weights using that same Sout. Therefore, reporting CLIP-Score on U45/SQUID/C60 as evidence of human perceptual quality and generalization is using the training objective as the test metric: the reported number is the quantity the method was optimized to increase, not an independent assessment of those datasets. Section IV.J concedes that the model 'may benefit from a more accurate selection of initialization prompts,' and no human validation is given for the three no-reference datasets.

full rationale

The central enhancement claim is not circular. The proposed network is NU2Net augmented with Lclip and LCR, and the full-reference results on U90 (PSNR 23.115 vs 22.669, SSIM 0.929 vs 0.924, LPIPS 0.133 vs 0.154) are measured with metrics that are not part of the loss or the CLIP perception model. The ablations in Table IV and the plug-in experiments on WaterNet, FUnIE, Shallow-UWnet, and PUGAN in Table VIII show consistent improvements from the added modules, so the main derivation is self-contained and externally checkable. The circularity is limited to the non-reference evaluation path: the CLIP perception model is trained on UEQAB MOS labels (Eq.4), used as the training loss (Eq.5) and as the negative-weighting criterion (Eq.6), and then the same model's score is reported as CLIP-Score in Table III to claim generalization on U45/SQUID/C60. That is a fitted quantity used as an evaluation measure without independent human validation on those datasets, and the paper's own limitation section concedes incomplete prompt optimization. No load-bearing self-citation chain or imported uniqueness theorem is involved, and the full-reference claim does not reduce to the fitted CLIP model. The appropriate score is therefore 4: partial circularity in one evaluation metric, while the central full-reference contribution retains independent content.

Assumptions & free parameters 8 free parameters · 5 assumptions · 0 invented entities

The central claim rests on the transferability of a CLIP-based quality model trained on MOS labels, on the reliability of pseudo ground-truth reference images, and on several hand-set loss weights. No new physical entities are introduced.

free parameters (8)
  • CLIP prompt embeddings T_p and T_n = Not reported (learned on UEQAB)
    Learned by minimizing MSE between softmax text-image similarity and MOS on UEQAB (Eq.4); final values are omitted, so the perception model cannot be directly reused.
  • alpha in Lclip = 0.975
    Hyperparameter in Eq.5 controlling the target quality margin above the reference; no sensitivity analysis is shown.
  • gamma in curriculum weights = 0.25
    Eq.6 weight margin for hard vs very-hard negatives; set by hand.
  • lambda1 (Lclip weight) = 0.025
    Loss balance in Eq.8; Table VII shows a peak near this value.
  • lambda2 (LCR weight) = 0.1
    Loss balance in Eq.8; Table VII shows a peak near this value.
  • Number of non-easy negatives z = 6 (7 total including easy negative)
    Table VI shows 7 total works best; chosen by experiment.
  • VGG feature layer weights xi_i = 1/32, 1/16, 1/8, 1/4, 1
    Used in Eq.7 for the five VGG-19 layers; taken directly from Zheng et al. [22] without re-tuning.
  • Initial prompt texts = "Clear Underwater photo.", "Turbid Underwater photo."
    Initialization for prompt learning; the paper notes that prompt initialization influences model effectiveness.
assumptions (5)
  • domain assumption CLIP's frozen image and text encoders provide a transferable representation for underwater perceptual quality.
    Central premise of the CLIP perception model; no direct validation on target datasets beyond UEQAB.
  • domain assumption UEQAB MOS scores are reliable ground-truth labels for perceptual quality.
    The perception model is trained entirely on these labels; no cross-check with independent user studies is provided.
  • domain assumption UIEB pseudo ground-truth images are valid training targets.
    Paper itself notes these images are subjectively selected by volunteers (Section I), yet uses them as Y for L1, Lclip, and the positive sample in LCR.
  • domain assumption VGG-19 features are valid perceptual distances for underwater enhancement.
    LCR uses VGG-19 L1 distances; this is inherited from [22] and not validated for underwater imagery.
  • domain assumption All baseline methods were trained fairly on the same datasets and devices.
    Authors state fair comparisons, but no code or training logs are provided to verify equal budgets and protocols.

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Cite this review

Pith. "Pith review of Unveiling the Underwater World: CLIP Perception Model-Guided Underwater Image Enhancement." pith.science (2026). https://pith.science/paper/5PEDV66X

@misc{pith2026250706234,
  author       = {Pith},
  title        = {Pith review of: Unveiling the Underwater World: CLIP Perception Model-Guided Underwater Image Enhancement},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5PEDV66X}},
  note         = {Machine review of arXiv:2507.06234}
}
read the original abstract

High-quality underwater images are essential for both machine vision tasks and viewers with their aesthetic appeal.However, the quality of underwater images is severely affected by light absorption and scattering. Deep learning-based methods for Underwater Image Enhancement (UIE) have achieved good performance. However, these methods often overlook considering human perception and lack sufficient constraints within the solution space. Consequently, the enhanced images often suffer from diminished perceptual quality or poor content restoration.To address these issues, we propose a UIE method with a Contrastive Language-Image Pre-Training (CLIP) perception loss module and curriculum contrastive regularization. Above all, to develop a perception model for underwater images that more aligns with human visual perception, the visual semantic feature extraction capability of the CLIP model is leveraged to learn an appropriate prompt pair to map and evaluate the quality of underwater images. This CLIP perception model is then incorporated as a perception loss module into the enhancement network to improve the perceptual quality of enhanced images. Furthermore, the CLIP perception model is integrated with the curriculum contrastive regularization to enhance the constraints imposed on the enhanced images within the CLIP perceptual space, mitigating the risk of both under-enhancement and over-enhancement. Specifically, the CLIP perception model is employed to assess and categorize the learning difficulty level of negatives in the regularization process, ensuring comprehensive and nuanced utilization of distorted images and negatives with varied quality levels. Extensive experiments demonstrate that our method outperforms state-of-the-art methods in terms of visual quality and generalization ability.

Figures

Figures reproduced from arXiv: 2507.06234 by the authors.

Figure 1
Figure 1. The framework of CLIP perception model. III. METHOD A. Overview The framework of our proposed method is demonstrated in [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. The proposed framework of UIE network. (a) The CLIP Perception Loss module [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Visual comparison of enhancement results for the U90 test set. From left to right, the original underwater image, MLLE [55], WaterNet [18], [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Visual comparison of enhancement results for the U45, SQUID, and C60 test set. From left to right, the original underwater image, MLLE [55], [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]
Figure 5
Figure 5. Figure 5: Visual effects of enhanced images obtained by schemes with individual [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]
Figure 6
Figure 6. Figure 6: Visual comparison between four UIE models without and with [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]
Figure 7
Figure 7. Figure 7: The saliency detection results by SVAM-Net [59] on the original [PITH_FULL_IMAGE:figures/full_fig_p011_7.png]

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Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.