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REVIEW 5 major objections 5 minor 2 cited by

Phaseformer: Phase-based Attention Mechanism for Underwater Image Restoration and Beyond

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

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Pith's one-line read A 1.77M-parameter transformer restores underwater images by attending to Fourier phase

desk verdict Plausible phase-attention idea for underwater restoration, but the headline SOTA claim rests on a table with an impossible SSIM value, so the empirical case is not yet made. read the letter →

arxiv 2412.01456 v1 pith:OVQAOQNB submitted 2024-12-02 cs.CV eess.IV

MD Raqib Khan , Anshul Negi , Ashutosh Kulkarni , Shruti S. Phutke , Santosh Kumar Vipparthi , Subrahmanyam Murala This is my paper · ORCID
classification cs.CVeess.IV
keywords Phaseformerunderwaterimagerestorationphase-basedattentionFourierphaselightweighttransformerlow-lightenhancementadaptivelossweighting
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 proposes Phaseformer, a lightweight transformer for restoring color and detail in degraded underwater images. Its central bet is that the Fourier phase of an image carries structural information that survives underwater degradation better than the amplitude does, so attention should be computed from phase-only queries and keys. The network pairs this phase-based self-attention with a phase-based skip block and trainable loss weights, and the authors report PSNR/SSIM of 25.98/0.928 on UIEB and 30.38/0.878 on UFO-120, above the compared methods, with leading UIQM on U45. They also show the same network improves low-light images and helps downstream detection and depth estimation.

What carries the argument

The phase extraction module (PEM) is the load-bearing mechanism: it computes the FFT of input features, discards the amplitude by setting it to 1, keeps the phase, and returns phase-only features via inverse FFT. These phase-only features become the queries and keys of a phase-based multi-head self-attention block, producing a transposed attention map of size channels-by-channels. An optimized phase attention block applies the same phase-only idea in the skip connections, using global average pooling and an adaptively sized 1D convolution to weight prominent structural features. A fourth component, adaptive loss weighting, makes the weights of Charbonnier, gradient, MS-SSIM, and perceptual losses trainable rather than hand-tuned.

What would settle it

Measure FFT phase error and amplitude error between paired clean and degraded underwater images, for example on UIEB training pairs; if the phase error is comparable to the amplitude error, the paper's core premise fails. Alternatively, train Phaseformer with added phase noise on the inputs and observe whether restoration quality drops as fast as it does under amplitude noise, or compare against the same transformer with randomly shuffled phase keys.

Watch

Extended reading notes

Core claim

The paper's claim is that a transformer can restore underwater images more effectively when its self-attention operates on phase-only features rather than on the raw features themselves. Concretely, a phase extraction module applies an FFT, sets the amplitude to 1, keeps the phase, and applies an inverse FFT; these phase-only maps serve as queries and keys, while values come from the input projection. The paper further claims that an optimized phase attention block in the skip connections propagates structural information from encoder to decoder without transferring degraded amplitude content, and that adaptively learned loss weights improve training over fixed weights. With 1.77 million parameters and 13.0 GFLOPs, the reported results on synthetic and real-world underwater benchmarks are consistently better than the compared state-of-the-art methods, and the same architecture improves low-light image enhancement.

Load-bearing premise

The whole design leans on the claim that underwater degradation attacks the Fourier amplitude of an image far more than its phase; if real-world scenes corrupt phase substantially, the attention mechanism loses its proposed advantage.

Editorial extensions

If this is right

  • If phase information is indeed the least-degraded part of an underwater image, then attention computed from phase-only queries and keys should restore structure without needing a large or deep network.
  • The reported parameter count and FLOPs suggest the architecture is light enough for onboard preprocessing on autonomous underwater vehicles, where computational budget is tight.
  • The same phase-based attention generalizes, per the paper, to low-light image enhancement, so the mechanism is not specific to water degradation.
  • Improved restoration transfers to downstream tasks: the authors demonstrate better fish detection with YOLO-v3 and better depth estimation with DPT on enhanced images.
  • Trainable loss weights remove a manual tuning step and, according to the ablation, outperform fixed weights on UIEB.

Reading between the lines

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

  • A testable extension left implicit by the paper is whether the phase-robustness argument transfers to other amplitude-dominant degradations such as haze, rain, or blur; running the same architecture on standard dehazing and deraining benchmarks would isolate whether the gain is phase-specific or just the transformer backbone.
  • Because the phase-only query and key discard amplitude entirely, color-critical scenes might lose useful amplitude cues; a variant that re-injects a small fraction of amplitude into the attention would test whether the phase-only choice is optimal or merely sufficient.
  • The paper's t-SNE evidence for phase robustness is qualitative; a quantitative stress test would be to add controlled phase noise versus amplitude noise to degraded inputs and measure which noise type degrades restoration performance faster.
  • The multi-resolution output with adaptive loss weighting is a general training recipe that could be transplanted to other image restoration networks even if the phase-based attention is replaced.
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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

5 major / 5 minor

Summary. The paper proposes Phaseformer, a lightweight transformer for underwater image restoration with 1.77M parameters. The core idea is to use phase-only features, obtained by setting the FFT amplitude to one in a phase extraction module, as queries and keys in multi-head self-attention, and to propagate encoder features through an optimized phase attention block in the skip connections. The training loss combines Charbonnier, gradient, MS-SSIM, and perceptual terms with learnable per-loss weights plus fixed high/low-resolution weights. The method is evaluated on UIEB, UFO-120, U45, UCCS, and SQUID, and is also applied to low-light enhancement and downstream detection/depth tasks. The central claim is that Phaseformer outperforms existing state-of-the-art methods on these benchmarks.

Significance. If the empirical claims hold, the paper makes a useful contribution: it introduces a phase-based attention mechanism that is coherent, lightweight, and supported by ablation studies showing consistent gains from each proposed component. The authors provide an explicit parameter/FLOPs comparison, a code availability statement, and evaluate across several standard underwater datasets, which strengthens the practical relevance. The main significance lies in the potential of phase-only attention for restoration tasks, but this significance is currently limited by evaluation inconsistencies and the lack of a quantitative validation of the core phase-degradation premise.

major comments (5)
  1. [Table 1, Section 4.4] The UIEB block of Table 1 reports SSIM = 15.78 for RGHS, which is impossible because SSIM is bounded above by 1. This is not a minor typo in isolation: Table 1 is the primary quantitative basis for the claim that Phaseformer outperforms existing SOTA methods. The authors must correct this value, recheck every other entry in Tables 1 and 2 with a standard evaluation script, and report the exact version of the metrics used, before the outperformance claim can be considered verified.
  2. [Sections 4.1 and 4.2] The dataset split counts are inconsistent across the two subsections. Section 4.1 states that UIEB uses 800 training pairs and 90 test pairs and that UFO-120 uses 1500 training pairs and 120 test pairs, while Section 4.2 states that augmentation produced 3000/4800 training image pairs and 120/90 testing image pairs for UFO-120/UIEB. The authors need to clarify whether these numbers refer to augmented copies, how many unique training images are used per dataset, and whether the test sets are identical to those used by the compared methods; otherwise the comparisons in Table 1 may not be on equal footing.
  3. [Tables 1 and 2] No error bars, standard deviations, or significance tests are reported for any metric. The claimed margins are small in several cases, e.g., SSIM 0.928 versus 0.917 for U-shape on UIEB, and the impossible RGHS SSIM value suggests the evaluation pipeline is not fully controlled. The authors should report multiple runs or at least provide released code and trained weights so that the margins can be independently reproduced.
  4. [Sections 1 and 3.1] The load-bearing premise that phase is less affected by underwater degradation than amplitude is supported only by the qualitative t-SNE plot in Figure 1 and by citations, including the authors' own prior work [34]. The paper does not quantify the degree to which phase is preserved under the actual degradation model used in training or evaluation, nor does it show that the learned attention maps indeed rely on phase information. Please add a quantitative experiment, such as phase/amplitude reconstruction error on degraded-clean pairs, or an attention-map analysis, to substantiate the motivation for the PEM module and to show that discarding amplitude information is not the sole source of the improvement.
  5. [Section 3.2, Eq. (5)] The adaptive kernel-size formula is ambiguous as written: k = | log2(C')/gamma + b/gamma |_odd. In particular, it is unclear whether the denominator gamma applies to the whole numerator or only to log2(C'), and the placement of the nearest-odd operation relative to the division should be stated unambiguously. This makes the OPAB module difficult to reimplement exactly as described.
minor comments (5)
  1. [Table 1] The method name 'Spectrofomer' is misspelled; it should be 'Spectroformer'.
  2. [Section 4.4] The text says 'Our method's performance aligns competitively with existing approaches,' which is much weaker than the abstract's claim that the approach 'outperforms existing SOTA methods.' Please make the wording consistent with the reported numbers.
  3. [References] References [70] and [71] appear to refer to the same paper, 'Deep retinex decomposition for low-light enhancement.' One should be removed or the citation should be unified.
  4. [Abstract and Section 1] The code availability statement in the abstract says 'Code is available at Phaseformer,' but no URL or repository identifier is provided. Please include a working link or at least a complete reference to the repository.
  5. [Table 4] The table header uses 'OPA' and 'OA' with checkmarks that are difficult to map to the five rows; please rename the columns or add a legend so the reader can identify which configuration is ablated in each row.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity found: the phase-sensitivity premise is grounded in independent references and the paper's own t-SNE visualization, and the benchmark evaluations are external to the method's construction.

full rationale

The paper's central design choice—phase-based self-attention and phase attention skip blocks—rests on the premise that phase is more robust to underwater degradation than amplitude. The paper cites the authors' earlier work [34] for this premise, but it also cites independent signal-processing texts [8, 21] and provides its own t-SNE evidence (Figure 1). Under the review rules, a citation counts as circular only if it is unverified and load-bearing; here the premise does not reduce to [34] alone. The phase extraction module (PEM) is a standard phase-only reconstruction (FFT, set M_f=1, IFFT) and is used as a feature transform, not as a disguised restatement of the output metric. The attention equations (1)-(3) and OPAB equation (4) define new computations whose components are ablated in Tables 4 and 5; no fitted parameter is renamed as a prediction. The headline SOTA claim is supported by comparisons against external baselines in Tables 1 and 2, so any error there (e.g., the impossible RGHS SSIM of 15.78, or the inconsistency between train-pair counts in Sections 4.1 and 4.2) is a correctness/verification concern, not a circularity. No self-definitional, fitted-input-as-prediction, imported-uniqueness, ansatz-smuggling, or renaming step was found.

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

The paper introduces no new physical or conceptual entities. It introduces an architectural recipe: phase-only attention features, a phase attention skip block, and learnable loss weights. The main free parameters are the learned attention temperature, the learned loss weights, and two hand-set kernel hyperparameters. The key axioms are the phase-robustness assumption and the assumption that zeroed amplitude still leaves useful structure, both of which are load-bearing and only weakly supported.

free parameters (4)
  • Attention temperature alpha = Learned, not reported
    Equation 3 uses a learned alpha to scale the softmax dot product before attention. No final value or distribution is given.
  • Loss weights Omega1 to Omega4 = 0.2741, 0.2222, 0.3357, 0.1680 at final epoch
    Equation 7 defines learnable weights for Charbonnier, gradient, MS-SSIM, and perceptual losses. These are trained on the UIEB dataset and the reported final values depend on that tuning.
  • High and low resolution loss weights Omega_H, Omega_L = 0.4 and 0.6
    Set empirically in Section 3.3, not learned, and required for the total loss in Equation 6.
  • Adaptive kernel hyperparameters gamma and b = gamma=2, b=1
    Used in Equation 5 to select the adaptive kernel size in the optimized phase attention block. These are hand-set constants with no ablation reported.
assumptions (4)
  • domain assumption Phase information of underwater images is less degraded by scattering and absorption than amplitude information.
    Used to justify phase-only queries and keys in Section 3.1 and phase-based skip propagation in Section 3.2. Supported only by the qualitative t-SNE in Figure 1 and by citations including the authors' own [34].
  • ad hoc to paper Setting FFT amplitude to 1 and inverse-transforming preserves enough structural content for attention.
    The phase extraction module in Section 3.1 discards amplitude entirely. No ablation tests whether keeping partial amplitude information would improve or hurt attention.
  • standard math The Restormer-style transposed attention and feed-forward network transfer successfully to underwater restoration.
    The architecture borrows multi-head transposed attention and FFN from Restormer [74, 75], assuming those components work as advertised in the new domain.
  • domain assumption The evaluation metrics and benchmark ground truths are valid for comparing underwater image restoration methods.
    All quantitative conclusions in Tables 1, 2, and 5 assume PSNR, SSIM, UIQM, NIQE, and BRISQUE are faithful measures and that the UIEB and UFO-120 references are correct.

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Cite this review

Pith. "Pith review of Phaseformer: Phase-based Attention Mechanism for Underwater Image Restoration and Beyond." pith.science (2026). https://pith.science/paper/OVQAOQNB

@misc{pith2026241201456,
  author       = {Pith},
  title        = {Pith review of: Phaseformer: Phase-based Attention Mechanism for Underwater Image Restoration and Beyond},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/OVQAOQNB}},
  note         = {Machine review of arXiv:2412.01456}
}
read the original abstract

Quality degradation is observed in underwater images due to the effects of light refraction and absorption by water, leading to issues like color cast, haziness, and limited visibility. This degradation negatively affects the performance of autonomous underwater vehicles used in marine applications. To address these challenges, we propose a lightweight phase-based transformer network with 1.77M parameters for underwater image restoration (UIR). Our approach focuses on effectively extracting non-contaminated features using a phase-based self-attention mechanism. We also introduce an optimized phase attention block to restore structural information by propagating prominent attentive features from the input. We evaluate our method on both synthetic (UIEB, UFO-120) and real-world (UIEB, U45, UCCS, SQUID) underwater image datasets. Additionally, we demonstrate its effectiveness for low-light image enhancement using the LOL dataset. Through extensive ablation studies and comparative analysis, it is clear that the proposed approach outperforms existing state-of-the-art (SOTA) methods.

Figures

Figures reproduced from arXiv: 2412.01456 by the authors.

Figure 1
Figure 1. t-SNE visualization of the Amplitude and Phase of clean [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Architectural schematic of the proposed underwater image enhancement network. The network comprises of [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Qualitative comparison of the proposed method (Ours) with existing SOTA methods for underwater image restoration on the [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Qualitative comparison of the proposed method (Ours) with existing SOTA methods for underwater image restoration on real [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Qualitative comparison of results obtained with various [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 8
Figure 8. Figure 8: Qualitative results analysis with the proposed (ours) and [PITH_FULL_IMAGE:figures/full_fig_p008_8.png]

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Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Single-Step Latent Diffusion for Underwater Image Restoration

    cs.CV 2025-07 conditional novelty 6.0 of 10

    SLURPP combines pretrained latent diffusion priors with a physics-based scene-medium decomposition to restore underwater images in one inference step, beating prior diffusion methods in speed and quality.

  2. DEEP-SEA: Deep-Learning Enhancement for Environmental Perception in Submerged Aquatics

    cs.CV 2025-08 unverdicted novelty 4.0 of 10

    DEEP-SEA, a dual-frequency self-attention network, reports state-of-the-art underwater image restoration on the EUVP and LSUI benchmarks.

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Pith tools

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