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REVIEW 3 major objections 5 minor 56 references

Underwater Image Enhancement with Cascaded Contrastive Learning

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read This paper claims that underwater images are best enhanced in two stages—color correction in Lab space, then haze removal—with the first stage's output serving as the negative sample in a second contrastive loss, so each stage…

desk verdict New twist (stage-1 output as stage-2 negative) undercut by own ablation; PSNR/SSIM deficit is waved away with a post hoc metric switch. read the letter →

arxiv 2411.10682 v1 pith:VSQM6CX7 submitted 2024-11-16 cs.CV

Yi Liu , Qiuping Jiang , Xinyi Wang , Ting Luo , Jingchun Zhou This is my paper · ORCID
classification cs.CV
keywords underwaterimageenhancementcascadedcontrastivelearningtwo-stagenetworkcolorcorrectionhazeremovalLabspacelossUIEBbenchmark
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 argues that underwater image enhancement should not be left to one network. It builds two cascaded subnetworks—a color correction network working in Lab color space and a haze removal network that receives the color-corrected image—and trains each with a contrastive loss that pulls the output toward a clear reference and pushes it away from a negative sample. The design's distinctive move is that the second stage's negative sample is the first stage's own output, so the network is trained to make the final image visibly better than the intermediate one. The authors report that this two-stage cascade beats single-stage deep learning methods on non-reference quality metrics (UIQM, UCIQE) and in visual comparisons across five real underwater benchmarks, while acknowledging that reference-based PSNR and SSIM scores are not the best.

What carries the argument

The machinery is a two-stage cascade: CC-Net operates on the $a,b$ channels of the Lab color space, learns a residual color difference $\Delta(I_{ab})$, and leaves the luminance channel $I_L$ untouched; HR-Net is an attention-based multi-scale fusion network using Selective Kernel Feature Fusion blocks to remove haze. The load-bearing training mechanism is the cascaded contrastive loss: in stage one, the raw image is the negative sample, and in stage two the color-corrected stage-one output $I^{CC}$ becomes the negative sample, with both contrastive losses comparing VGG-19 feature distances in Eqs. (11) and (23) via ratios $\|E_i(I^{out})-E_i(I^{REF})\|_1 / \|E_i(I^{out})-E_i(I^{neg})\|_1$. This forces the output closer to the reference and farther from the intermediate result, which is what the paper claims incrementally improves the lower and upper bounds of enhancement.

What would settle it

Run a paired-preference study on UIEB-T90 comparing the full CCL-Net against its own 'w/o CC-Net' ablation: if human viewers do not prefer the two-stage color-then-haze output over the single-stage haze output, the central claim that the cascade improves visual quality would be refuted.

Watch

Extended reading notes

Core claim

The central claim is that a divide-and-conquer cascade with cascaded contrastive learning progressively lifts the quality of underwater images: CC-Net removes color cast while leaving luminance untouched, and HR-Net then removes haze from the color-corrected image. Each stage is constrained by a hybrid loss: an explicit color or SSIM loss plus a contrastive loss built on VGG-19 features, where the negative sample for stage two is the stage-one output rather than the raw underwater image. The authors contend that using a better-quality negative sample raises the lower bound of enhancement, so the final result is guaranteed to improve on the intermediate color-corrected image, and they support this with visual comparisons and UIQM/UCIQE scores on UIEB-T90, UIEB-C60, EUVP-T515, SQUID-T16, and RUIE-T78.

Load-bearing premise

The load-bearing premise is that color cast and haze are separable degradations, and that making the first stage's color-corrected output the second stage's negative sample improves the final image rather than merely steering it away from the reference.

Editorial extensions

If this is right

  • Dividing underwater degradation into a color-correction stage and a haze-removal stage, each with its own tailored loss, is more effective than a single network trying to balance several losses at once.
  • Using the stage-one color-corrected output as the negative sample in the second contrastive loss ensures the final image is trained to be better than the intermediate one, raising the lower bound of enhancement.
  • The full model achieves the highest UIQM among all compared methods on UIEB-T90 and on EUVP-T515 and SQUID-T16, and is always in the top four for UCIQE across the four generalization datasets, supporting the claim of robustness across diverse real underwater scenes.
  • With 0.55M parameters and 0.06s running time, the two-stage network is comparable to or faster than several single-stage deep models, so the cascade does not impose a prohibitive computational cost.

Reading between the lines

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

  • The cascaded-negative recipe is a general training strategy: any restoration task with two ordered degradations (for instance low light followed by haze) could reuse the same idea of making the first stage's output the negative sample for the second stage.
  • The paper's ablation suggests the color-then-haze order is chosen for visual quality, not reference fidelity: removing CC-Net raises UIEB-T90 PSNR from 20.18 to 22.01 and SSIM from 0.866 to 0.905, so a deployment that optimizes PSNR/SSIM might prefer the single-stage haze network.
  • A natural test of the lower-bound argument is to vary the negative-sample curriculum—for example, using the raw image, the color-corrected image, or an exponential moving average of intermediate outputs as negatives—and measure whether UIQM/UCIQE and human preference move monotonically with negative-sample quality.
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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 / 5 minor

Summary. The paper proposes CCL-Net, a two-stage underwater image enhancement network in which a color correction stage (CC-Net) is followed by a haze removal stage (HR-Net), with each stage trained under a hybrid loss that includes a contrastive loss. The raw image serves as the negative sample in the stage-1 contrastive loss, and the stage-1 output serves as the negative sample in the stage-2 contrastive loss. The authors claim that this cascaded design incrementally improves both the lower and upper bounds of the enhancement result and achieves superior performance over state-of-the-art methods on several benchmarks, including UIEB-T90, UIEB-C60, EUVP-T515, SQUID-T16, and RUIE-T78.

Significance. The conceptual idea of using higher-quality intermediate outputs as negative samples in a cascaded contrastive learning framework is interesting and could, in principle, be a useful training strategy for UIE. The paper also provides a large set of experiments, visual comparisons, and an ablation study, and it promises to release source code. However, the central claim of progressive improvement is not supported by the paper's own quantitative results: the full model is worse on the reference-based metrics PSNR/SSIM on UIEB-T90 than every ablated variant that the paper argues is inferior, and the justification for dismissing PSNR/SSIM is inconsistent with their use in the main comparison. As a result, the evidence does not substantiate the claimed advantage of the two-stage cascade or of the specific contrastive negative sampling strategy.

major comments (3)
  1. [Table III vs. Table I, Section IV-D and IV-E] The central claim of incremental improvement is directly contradicted by the paper's own ablation results. On UIEB-T90, the full CCL-Net obtains PSNR 20.181 and SSIM 0.866, while removing CC-Net (w/o CC-Net) gives PSNR 22.006 and SSIM 0.905, removing the contrastive loss (w/o CL) gives 21.724/0.891, and replacing the stage-2 negative with the raw image (RAN) gives 21.845/0.890. Thus every ablated variant that the paper argues is worse actually scores higher on the same reference-based metrics used in Table I to compare with other methods. The paper's response that PSNR/SSIM are unreliable for underwater images is inconsistent, because Table I uses these very metrics to argue that CCL-Net outperforms traditional methods, and the paper does not provide a principled reason why PSNR/SSIM should be trusted for comparisons against other deep learning methods but not for comparisons among its own ablated variants.
  2. [Eqs. (11), (23) and the 'lower/upper bound' claim in Sections I and III] The paper repeatedly claims that the cascaded contrastive losses 'incrementally improve the lower and upper bound of the enhancement result' and that using the stage-1 output as the stage-2 negative yields 'a better lower bound.' This is never directly measured: no intermediate-stage quality bounds are reported, and no theoretical argument is given. The loss in Eq. (23) explicitly pushes the final output away from the stage-1 output in VGG feature space, so the training objective itself defines 'better' in that learned feature space. The paper does not show that minimizing this loss improves any downstream quality metric or guarantees monotone improvement of image quality. Without such evidence, the 'better lower bound' claim is a design choice, not an empirical finding.
  3. [Table II, Section IV-D] The generalization claim rests on non-reference metrics computed on author-selected subsets (SQUID-T16, RUIE-T78) with no variances, confidence intervals, or significance tests. The reported UIQM differences are often small (e.g., 2.936 vs. 2.837 on EUVP-T515; 3.168 vs. 3.237 on RUIE-T78 for TACL) and the UCIQE rankings vary across datasets (1st on RUIE-T78 but outside the top three on SQUID-T16). The paper itself argues in Section IV-D that UCIQE 'cannot truly measure human perception' and that non-reference metrics should be combined with subjective judgments, yet the quantitative claim of superiority is based on exactly these metrics. The evidence is therefore insufficient to support the stated conclusion that CCL-Net 'outperforms all single-stage framework deep learning methods except TACL.'
minor comments (5)
  1. [Section I, Introduction] There is a grammatical error in the phrase 'an haze removal sub-network'; it should be 'a haze removal sub-network.'
  2. [Eqs. (11) and (23)] The scaling factor s is set to 100 in Eq. (11) but to 1 in Eq. (23), and the choice is described only as 'empirically set.' The paper should report the sensitivity of the results to s, since this is a free hyperparameter that could affect the relative weighting of the contrastive loss.
  3. [Section IV-D] The paper uses UCIQE as a primary metric in Tables I and II but also cites [56] to argue that UCIQE 'cannot truly measure human perception.' This is confusing: if UCIQE is unreliable, the paper should not rank its method on UCIQE without qualification; if it is reliable, then the low rankings on several datasets should be taken seriously.
  4. [Figure 5 caption] The caption states that 'the numbers on the top-left corner of each image refer to its PSNR/SSIM values,' but in the compiled text these numbers are not visible in the figure. Either the figure should be regenerated with legible numbers or the caption should be clarified.
  5. [Section IV-A, Implementation Details] The description of the training procedure says the second stage is trained using the 800 enhanced images generated by the first stage, but the ablation 'w/o CC-Net' trains HR-Net directly on raw images. The paper should clarify whether all other training settings (learning rate, epoch, loss weights, etc.) were kept identical for the ablation models to ensure a fair comparison.

Circularity Check

2 steps flagged · score 4.0 of 10

The 'better than intermediate' claim is partly built into the stage-2 contrastive loss (Eq. 23), and the paper leans on a self-cited prior work to discount reference-based metrics that contradict it; external UIQM/visual benchmarks keep the central claim partly independent.

  1. self definitional [Abstract; Sec. III-C2, Eq. (23)]
    "While in the second stage, the enhanced results rather than the raw underwater images of the first color correction stage are used as the negative samples for building the second contrastive loss, thus ensuring the final enhanced results of the second haze removal stage are better than the intermediate color corrected results."

    In Eq. (23), the stage-2 contrastive loss is L_HR_ctr = (1/s) sum_i w_i ||E_i(I_HR)-E_i(I_REF)||_1 / ||E_i(I_HR)-E_i(I_CC)||_1, with I_CC the CC-Net output. Minimizing this ratio explicitly pushes I_HR away from I_CC in VGG feature space, so 'final results are better than the intermediate color corrected results' is the training objective itself rather than an empirical consequence. The paper's reference-based check does not confirm the claim: on UIEB-T90 (Table III) full CCL-Net gives PSNR 20.181/SSIM 0.866, while removing CC-Net gives 22.006/0.905, so support for 'better' reduces to the non-reference UIQM/UCIQE scores plus the loss objective that defines 'better'.

  2. self citation load bearing [Sec. IV-D, Quantitative Comparisons]
    "As reported in previous works [21] [56], the UCIQE metric cannot truly measure human perception of underwater enhanced image quality."

    This sentence is used to justify discounting UCIQE results in which SMBL and MLLE beat CCL-Net (e.g., UIEB-T90 UCIQE 0.455/0.468 vs 0.464; UIEB-C60 0.439/0.464 vs 0.434). Reference [56] is 'Y. Kang, Q. Jiang, C. Li, W. Ren, H. Liu, and P. Wang, "A perception-aware decomposition and fusion framework for underwater image enhancement", IEEE TCSVT 2022', where Q. Jiang is the corresponding author of the present paper. The load-bearing move of dismissing a contradictory metric thus rests partly on a self-cited prior work; [21] and the visual comparisons give some independent support, but the metric choice is not independently re-derived here.

full rationale

The central two-stage improvement claim is not wholly circular: the network is evaluated on external benchmarks (UIEB-C60, EUVP, SQUID, RUIE) with public code and the UIQM/visual comparisons are separate from the training losses. However, two load-bearing steps weaken the derivation. First, the abstract's guarantee that stage 2 makes the output 'better than the intermediate color corrected results' is built into Eq. (23), which uses the stage-1 output as the negative sample; the claim is therefore partly the training objective restated as a result. Second, the paper's own ablation (Table III) shows the full model is worse on PSNR/SSIM than the model without CC-Net (20.181/0.866 vs 22.006/0.905), and the paper pivots to non-reference metrics, leaning on a self-cited prior work [56] to dismiss UCIQE. These issues raise the circularity score to 4: there is partial self-definitional content and a load-bearing self-citation, but independent benchmark content remains, so this is not a case of the derivation being equivalent to its inputs.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The central empirical claims rest on several hand-set hyperparameters (loss weights, scaling factors, VGG feature weights) and domain assumptions about degradation separability and metric validity. The most consequential assumption, that color cast and haze can be corrected independently in sequence, is challenged by the paper's own ablation results.

free parameters (5)
  • lambda_CC = 0.5
    Weight balancing color loss and contrastive loss in Eq. 4; set empirically without sensitivity analysis.
  • lambda_HR = 0.5
    Weight balancing SSIM loss and contrastive loss in Eq. 13; set empirically.
  • scaling factor s for stage 1 = 100
    Normalization factor in Eq. 11; hand-set to match scale of L_color.
  • scaling factor s for stage 2 = 1
    Normalization factor in Eq. 23; hand-set with no stated justification.
  • VGG-19 layer weights w1..w5 = 1/32, 1/16, 1/8, 1/4, 1
    Weights for layers 1,3,5,9,13 of VGG-19 in the contrastive losses; adopted from prior work and not tuned for underwater imagery.
assumptions (4)
  • domain assumption Underwater degradation separates into a color-cast component correctable in Lab color channels, and a haze component removable from the color-corrected RGB image; correcting color without modifying the luminance channel does not harm haze removal.
    Stages 1 and 2 are designed and trained sequentially on this decomposition (Eqs. 1-3, 12, 23). The ablation in Table III shows removing CC-Net improves PSNR/SSIM on UIEB-T90, suggesting this separation is not fully valid for reference-based fidelity.
  • domain assumption UIQM and UCIQE, especially UIQM, are reliable proxies for perceived underwater image quality and should be weighted over PSNR/SSIM when they disagree.
    Section IV-D uses this premise to claim superiority despite lower PSNR/SSIM. No user study validates this preference in the reported comparisons.
  • domain assumption The pseudo-reference images in UIEB are accurate enough ground truths for training and evaluation.
    The method trains and evaluates against these references (Section IV-A); if the references are biased, the color and haze losses encode that bias.
  • domain assumption VGG-19 features measure perceptually meaningful differences for underwater enhancement, and the contrastive ratios in Eqs. 11 and 23 behave as intended across the training batch.
    The contrastive losses rely on the standard behavior of VGG-19 features from [29,32] without revalidation for underwater imagery.

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Cite this review

Pith. "Pith review of Underwater Image Enhancement with Cascaded Contrastive Learning." pith.science (2026). https://pith.science/paper/VSQM6CX7

@misc{pith2026241110682,
  author       = {Pith},
  title        = {Pith review of: Underwater Image Enhancement with Cascaded Contrastive Learning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VSQM6CX7}},
  note         = {Machine review of arXiv:2411.10682}
}
read the original abstract

Underwater image enhancement (UIE) is a highly challenging task due to the complexity of underwater environment and the diversity of underwater image degradation. Due to the application of deep learning, current UIE methods have made significant progress. Most of the existing deep learning-based UIE methods follow a single-stage network which cannot effectively address the diverse degradations simultaneously. In this paper, we propose to address this issue by designing a two-stage deep learning framework and taking advantage of cascaded contrastive learning to guide the network training of each stage. The proposed method is called CCL-Net in short. Specifically, the proposed CCL-Net involves two cascaded stages, i.e., a color correction stage tailored to the color deviation issue and a haze removal stage tailored to improve the visibility and contrast of underwater images. To guarantee the underwater image can be progressively enhanced, we also apply contrastive loss as an additional constraint to guide the training of each stage. In the first stage, the raw underwater images are used as negative samples for building the first contrastive loss, ensuring the enhanced results of the first color correction stage are better than the original inputs. While in the second stage, the enhanced results rather than the raw underwater images of the first color correction stage are used as the negative samples for building the second contrastive loss, thus ensuring the final enhanced results of the second haze removal stage are better than the intermediate color corrected results. Extensive experiments on multiple benchmark datasets demonstrate that our CCL-Net can achieve superior performance compared to many state-of-the-art methods. The source code of CCL-Net will be released at https://github.com/lewis081/CCL-Net.

Figures

Figures reproduced from arXiv: 2411.10682 by the authors.

Figure 1
Figure 1. The enhanced results by proposed CCL-Net. (a) is the raw under [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Architecture of the proposed CCL-Net. It consists a color correction stage and a haze removal stage. In the first color correction stage, a raw underwater [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. The schematic illustration of CC-Net. The CC-Net learns the difference [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (10 more)
Figure 4
Figure 4. Figure 4: The schematic illustration of HR-Net. An attention-based multi-scale [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: Visual comparisons on underwater images with pronounced greenish and bluish tone from UIEB-T90. The numbers on the top-left corner of each [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: Visual comparisons on challenging underwater images with predominant greenish, bluish, and yellowish tones, as well as low brightness from [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 7
Figure 7. Figure 7: Visual comparisons on underwater images with greenish color deviation and low contrast from EUVP-T515. [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]
Figure 8
Figure 8. Figure 8: Visual comparisons on underwater images with different levels of bluish color deviation from SQUID-T16. [PITH_FULL_IMAGE:figures/full_fig_p009_8.png]
Figure 9
Figure 9. Figure 9: Visual comparisons on underwater images with four degradation types including hazy, yellowish, bluish, and greenish color deviations from RUIE-T78. [PITH_FULL_IMAGE:figures/full_fig_p011_9.png]
Figure 10
Figure 10. Figure 10: Ablation study of the contributions of each key component. Our full [PITH_FULL_IMAGE:figures/full_fig_p012_10.png]
Figure 11
Figure 11. Figure 11: Ablation study of the contributions of each key component. Our full [PITH_FULL_IMAGE:figures/full_fig_p012_11.png]
Figure 12
Figure 12. Figure 12: Unnatural colors introduced by CCL-Net on extreme low light [PITH_FULL_IMAGE:figures/full_fig_p013_12.png]
Figure 13
Figure 13. Figure 13: Inconsistent colors generated by CCL-Net on the same category or [PITH_FULL_IMAGE:figures/full_fig_p013_13.png]

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.