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REVIEW 5 major objections 5 minor 69 references

Retinex-guided Histogram Transformer for Mask-free Shadow Removal

T0 review · 5 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read A compact Retinex-guided transformer removes shadows without masks and matches far larger models.

desk verdict A plausible efficiency story and one genuinely new attention mechanism, buried under an experimental section copy-pasted from the authors' own ShadowRefiner paper — unverifiable as submitted. read the letter →

arxiv 2504.14092 v1 pith:GWNAB7ND submitted 2025-04-18 cs.CV

Wei Dong , Han Zhou , Seyed Amirreza Mousavi , Jun Chen This is my paper · ORCID
classification cs.CV
keywords shadowremovalmask-freeRetinextheoryhistogramself-attentionCNN-Transformerhybridimagerestorationilluminationestimationefficientdeeplearning
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

ReHiT is a mask-free shadow removal network whose central claim is that shadow removal is best organized as two separate restoration problems: one for reflectance (intrinsic object color) and one for illumination (lighting). Guided by Retinex theory, the network estimates approximate inverse maps that split the shadowed image into these two components, then restores each with a hybrid CNN-Transformer encoder-decoder whose histogram-based attention is modulated by the estimated illumination. The paper argues this design reaches the quality of much larger mask-free methods, reporting PSNR/SSIM/LPIPS comparable to ShadowRefiner on ISTD, ISTD+, and WSRD+ while using 17.5M parameters and 66.4 GFLOPs, roughly 17x fewer parameters and 4x fewer FLOPs than that baseline. Because it needs no shadow mask and was trained only on the NTIRE 2025 Shadow Removal Challenge training set, the result speaks to real-world shadow removal under imperfect annotations and limited compute.

What carries the argument

The load-bearing component is the Illumination-Guided Histogram Transformer Block (IG-HTB), specifically its illumination-guided histogram self-attention (IG-HSA). Standard self-attention uses a fixed attention range; IG-HSA instead sorts spatial elements into histogram bins with equal pixel counts and computes attention within and across bins, giving the model a flexible attention span for shadows of varying size and softness. The estimated illumination map is injected to modulate the attention, so the block can concentrate on regions of abrupt lighting change. Around this block, each IG-HCT encoder/decoder level also contains a Dilated Residual Dense Block (DRDB) for residual dense features and a Semantic-aligned Scale-Aware Module (SAM) for multi-scale fusion. The dual-branch Retinex decomposition of Eq. 3 is what turns shadow removal into two sub-problems; IG-HSA is what makes each sub-problem tractable with a small network.

What would settle it

Take the trained model and set the estimator to a constant identity ($\bar L=\bar R=1$) so that $R'=L'=I^{Sh}$; if the PSNR on WSRD+ stays near the reported 26.15 dB rather than falling to the 25.86 dB of the 'w/o dual-branch' ablation, the Retinex guidance is not the source of the gain. The paper's own ablation table supplies the exact number this variant must beat to confirm the mechanism.

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Extended reading notes

Core claim

The paper's central claim is that a Retinex-guided dual-branch pipeline can make a compact CNN-Transformer competitive with large pre-trained mask-free shadow removers. The shadowed image is modeled as $I^{Sh}=(R_{GT}+\hat R)\odot(L_{GT}+\hat L)$; assuming a Retinex estimator yields approximate inverses $\bar L$ and $\bar R$ satisfying $\bar L\odot L_{GT}=1$ and $\bar R\odot R_{GT}=1$, multiplying the input by these inverses produces near-clean reflectance $R'=I^{Sh}\odot\bar L$ and illumination $L'=I^{Sh}\odot\bar R$. Two instances of the Illumination-Guided Hybrid CNN-Transformer (IG-HCT) predict the residual degradations in $R'$ and $L'$, and the refined outputs are multiplied to obtain $I_{out}=R_{out}\odot L_{out}$. On benchmarks, ReHiT reports top-tier mask-free numbers: 28.81 dB PSNR on ISTD, 31.16 dB on ISTD+, and 26.15 dB on WSRD+, and its WSRD+ ablation attributes part of the gain to the dual-branch design and to the illumination-guided histogram attention.

Load-bearing premise

The whole framework depends on an unshown preprocessing step that separates each image into reflectance and illumination layers; the paper borrows this estimator from earlier work and never evaluates it on its own, so an inaccurate estimator would collapse the two branches into one and remove the claimed benefit of the lighting/color separation.

Editorial extensions

If this is right

  • If the results hold, mask-free shadow removal no longer needs a separate shadow-detection stage or manual masks, removing a major barrier to deployment on arbitrary real-world images.
  • The efficiency numbers (17.5M parameters, 66.4 GFLOPs) imply shadow removal can run on a single commodity GPU or edge device while retaining near-top quality, instead of requiring the multi-hundred-GFLOP budgets of large ConvNeXt-based competitors.
  • On WSRD+, where precise masks are unavailable and only estimated masks exist, ReHiT's 26.15 dB PSNR exceeds the mask-based ShadowFormer's 25.44 dB, suggesting mask-free methods can beat mask-dependent ones precisely where masks are hardest to obtain.
  • The WSRD+ ablation shows the dual-branch Retinex pipeline contributes roughly 0.29 dB PSNR and the IG-HTB roughly 0.41 dB; if these gains replicate, each architectural choice is load-bearing rather than cosmetic.
  • Training only on the NTIRE 2025 challenge set shows competitive shadow removal can be obtained without extra training data, large-scale pre-training, or multi-stage training pipelines.

Reading between the lines

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

  • A testable extension is to sweep the number of histogram bins in IG-HSA: if flexible attention span is the real cause of the gain, there should be an optimal bin count that tracks the spatial irregularity of shadows, with performance degrading on both sides.
  • The same reflectance-illumination split could transfer to low-light enhancement, dehazing, and exposure correction, since those tasks also fit the Retinex decomposition; the paper's citation of ECMamba already points in that direction.
  • The unspecified Retinex estimator is the main hidden variable: re-running the pipeline with the estimator frozen versus trained jointly, or replaced by a simple off-the-shelf decomposition, would reveal how much of the reported performance belongs to the estimator and how much to the IG-HCT modules.
  • Because the same IG-HCT design serves as both $M_R$ and $M_L$, the illumination branch's output could be tested as a zero-shot shadow locator: regions where $L'$ deviates most from their surroundings might localize shadows without any mask supervision.
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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 ReHiT, a mask-free shadow removal network that combines a dual-branch Retinex decomposition with an illumination-guided hybrid CNN-Transformer architecture. The method is evaluated on ISTD, ISTD+, WSRD+, and the NTIRE 2025 Shadow Removal Challenge, where the authors report competitive quality at 17.5M parameters and 66.4 GFLOPs, achieving 7th place in the fidelity track.

Significance. If substantiated, the reported efficiency-quality trade-off would be practically useful, and the authors do ship code. The design, however, reuses substantial components from the authors' previous works (ECMamba [13] and ShadowRefiner [11]) and from Sun et al. [42], so the novelty is incremental. More importantly, the experimental evidence as presented is internally contradictory and missing key technical details, so the central claim cannot currently be assessed.

major comments (5)
  1. [Section 4 (after Table 1)] The experimental section is internally inconsistent: it contains a large verbatim block from the authors' earlier ShadowRefiner paper, including text claiming 'our ShadowRefiner demonstrates superior performances,' a table captioned 'NTIRE 2024 Perceptual Track,' and an ablation table headed 'ShadowRefiner (Ours),' interleaved with the ReHiT narrative. As a result, Table 2 appears twice (once as model complexity, once as NTIRE 2024 ranking) and Table 3 appears twice (once as a ShadowRefiner ablation, once as the NTIRE 2025 ranking). No statement identifies which rows correspond to ReHiT, so the 'Ours (ReHiT)' numbers in Table 1 cannot be unambiguously attributed to the described model.
  2. [Section 3.1, Eq. (3)] The entire dual-branch construction depends on a Retinex estimator E that produces (bar-R, bar-L, F_i) satisfying bar-L ⊙ L_GT ≈ 1 and bar-R ⊙ R_GT ≈ 1. This estimator is never described in the manuscript; the only reference is [13], the authors' own ECMamba paper. The reader cannot determine the estimator's architecture, training data, or whether it is shared between the two branches, yet it is the basis for the reflectance/illumination separation and for the illumination guidance in IG-HSA. Without this component, the method is not reproducible and the ablation 'w/o dual-branch pipeline' in Table 4 is uninterpretable.
  3. [Section 4.2, Table 1 and Section 4.4, Table 4] All reported numbers are from a single training run with no error bars, multiple seeds, or significance tests. The claimed advantages over ShadowRefiner are 0.06-0.13 dB PSNR, while ReHiT is slightly worse on SSIM and LPIPS in two of the three datasets, and the ablations in Table 4 show performance drops of only 0.18-0.41 dB PSNR. These deltas are within typical run-to-run variation for image restoration networks; as presented, the results do not support the claims that ReHiT is competitive or that each component contributes meaningfully.
  4. [Section 4.3, Table 3] The NTIRE 2025 entry is listed as 'Oath (ours)' in Table 3, while the paper's method is named ReHiT; the relationship between these names is never explained. Furthermore, the paper claims 'fastest inference speeds among top-ranked entries,' but no runtime or latency measurements appear anywhere in the manuscript; Table 3 reports only parameter counts. The efficiency claim is therefore unsupported by the presented data.
  5. [Sections 4.1 and 4.3] It is never stated which training data and protocol produced the Table 1 results versus the NTIRE 2025 entry. Section 4.1 describes training on a single RTX 3090Ti GPU with specific augmentations and losses but does not mention the NTIRE dataset, while Section 4.3 states the model was trained solely on the NTIRE 2025 challenge set. The connection between the model evaluated in Table 1 and the model in Table 3 is missing, so the central efficiency-quality claim cannot be verified from the manuscript.
minor comments (5)
  1. [Abstract] The abstract contains a duplicated phrase: 'multi-scale semantic fusion, multi-scale semantic fusion.'
  2. [Figure 3 and Figure 4 captions] The captions for these figures refer to 'our ShadowRefiner' instead of 'our ReHiT,' which is inconsistent with the paper's method name.
  3. [Table 2 (NTIRE 2024) and Table 3 (ShadowRefiner ablation)] These table captions are clearly leftovers from the authors' previous ShadowRefiner paper and should be removed or corrected.
  4. [Section 4.1, loss function] The paper mentions 'additional constraints [13]' as part of the loss but never specifies what these constraints are; they should be defined or precisely referenced.
  5. [Code link] The code repository is linked as github.com/dongw22/oath while the model is named ReHiT; the relationship between these names should be clarified.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation; external benchmarks carry the central claims.

full rationale

The claimed derivation chain in Sec. 3.1 takes the dual-branch Retinex formulation from the authors' prior ECMamba [13] with explicit citations ('Therefore as in [13] perturbations ( ˆR and ˆL) are introduced to model these shadowed images', 'additional constraints [13] are used for optimization supervision'). This is architectural borrowing, not a circular reduction: Eq. 3 is not derived from the target metric, and [13] is a published prior that does not assume the present shadow-removal result. The central performance and efficiency claims are supported by external benchmarks (ISTD/ISTD+/WSRD+ comparisons and the NTIRE 2025 challenge ranking table), not by the paper's own fitted values or by the cited prior. The Retinex estimator E is not described in the manuscript, which is a reproducibility gap rather than a circular step; the duplicated ShadowRefiner text and duplicate table numbers are manuscript-integrity problems that affect verifiability but do not make any prediction equivalent to its input by construction. No fitted parameter is renamed as a prediction, no uniqueness theorem is imported, and no ansatz is disguised as a consequence.

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

The paper's central architecture depends on the Retinex decomposition borrowed from the authors' ECMamba [13] and on hand-chosen hyperparameters that are not reported. No new physical entities are introduced.

free parameters (3)
  • Histogram bin count and bin assignment in IG-HSA
    Categorizing spatial elements into bins is central to IG-HSA (Sec. 3.3), but the number of bins and the assignment criterion are never specified, so they are hand-chosen ad hoc at implementation time.
  • Loss weighting coefficients
    The training loss combines L1, multi-scale SSIM, structure loss [67], and 'additional constraints [13]' (Sec. 4.1), but the relative weights are not reported.
  • Illumination guidance modulation strength in IG-HSA
    How illumination information modulates attention (multiplicative, additive, or learned scalar) is not formalized in the paper.
assumptions (3)
  • domain assumption Image formation obeys the Retinex decomposition I = R * L, with independent reflectance and illumination.
    Classic Retinex theory (Land 1977) is assumed throughout Sec. 3.1; the shadowed image model of Eqs. 1-2 rests on it and on the perturbation model of [13].
  • ad hoc to paper An inverse Retinex estimator can recover maps Lbar and Rbar such that Lbar * L_GT = 1 and Rbar * R_GT = 1.
    Sec. 3.1 explicitly says 'under the assumption that we can approximate Lbar and Rbar via Retinex estimator', but provides no estimator details or validation.
  • domain assumption Histogram binning of features preserves the information needed for restoration.
    IG-HSA relies on sorting features into bins and attending within and across bins (Sec. 3.3, following Sun et al. [42]); the paper assumes this grouping does not lose critical spatial information.

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Cite this review

Pith. "Pith review of Retinex-guided Histogram Transformer for Mask-free Shadow Removal." pith.science (2026). https://pith.science/paper/GWNAB7ND

@misc{pith2026250414092,
  author       = {Pith},
  title        = {Pith review of: Retinex-guided Histogram Transformer for Mask-free Shadow Removal},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GWNAB7ND}},
  note         = {Machine review of arXiv:2504.14092}
}
read the original abstract

While deep learning methods have achieved notable progress in shadow removal, many existing approaches rely on shadow masks that are difficult to obtain, limiting their generalization to real-world scenes. In this work, we propose ReHiT, an efficient mask-free shadow removal framework based on a hybrid CNN-Transformer architecture guided by Retinex theory. We first introduce a dual-branch pipeline to separately model reflectance and illumination components, and each is restored by our developed Illumination-Guided Hybrid CNN-Transformer (IG-HCT) module. Second, besides the CNN-based blocks that are capable of learning residual dense features and performing multi-scale semantic fusion, multi-scale semantic fusion, we develop the Illumination-Guided Histogram Transformer Block (IGHB) to effectively handle non-uniform illumination and spatially complex shadows. Extensive experiments on several benchmark datasets validate the effectiveness of our approach over existing mask-free methods. Trained solely on the NTIRE 2025 Shadow Removal Challenge dataset, our solution delivers competitive results with one of the smallest parameter sizes and fastest inference speeds among top-ranked entries, highlighting its applicability for real-world applications with limited computational resources. The code is available at https://github.com/dongw22/oath.

Figures

Figures reproduced from arXiv: 2504.14092 by the authors.

Figure 1
Figure 1. Our method demonstrates effective shadow removal from low-quality images and achieves the 7th place in the fidelity track. [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. The overall pipeline of our ReHiT. Our solution employs dual-branch Retinex-based pipeline, each branch is dedicated to the restoration of reflectance and illumination map, respectively. The Illumination-Guided Hybrid CNN-Transformers (IG-HCT) module is developed as the primary restoration network. Besides, the Illumination-Guided Histogram Transformer Block (IG-HTB) with the illumination-guided histogram self-atten… view at source ↗
Figure 3
Figure 3. Visual comparisons on the ISTD dataset [ Figure 3. Visual comparisons on the ISTD dataset [ [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Visual comparisons on the ISTD+ dataset[ Figure 4. Visual comparisons on the ISTD+ dataset [2 [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: We can see that our ShadowRefiner achieves no blifihdl ffiif diff performance utilize extra data for training, or includes more [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]

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Reference graph

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Pith tools

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