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REVIEW 3 major objections 6 minor 2 cited by

MaIR: A Locality- and Continuity-Preserving Mamba for Image Restoration

T0 review · 3 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read MaIR claims a Mamba restoration network can preserve both locality and continuity during scanning, and reports the best results across 40 baselines on 14 datasets.

desk verdict A solid Mamba-restoration paper with a plausible scanning design and broad experiments, but the SOTA claim is overstrong and the ablation margins need variance reporting. read the letter →

arxiv 2412.20066 v2 pith:CJ2HWH2C submitted 2024-12-28 cs.CV

Boyun Li , Haiyu Zhao , Wenxin Wang , Peng Hu , Yuanbiao Gou , Xi Peng This is my paper · ORCID
classification cs.CV
keywords Mambaselectivestatespacemodelsimagerestorationsuper-resolutiondenoisingdeblurringdehazingscanningstrategy
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 Mamba-based image restoration fails not because of the selective scan itself but because of how images are flattened into 1D sequences and how those sequences are recombined. The authors propose MaIR, which scans the image in nested S-shaped stripes to keep nearby pixels close in the sequence while preserving the smooth left-to-right, top-to-bottom order of natural images, and then merges the four scan directions with channel-wise attention instead of simple addition. Trained on standard benchmarks, MaIR reports the best PSNR/SSIM among 40 baselines on super-resolution, denoising, deblurring, and dehazing across 14 datasets. If the result holds, scan-path design and sequence aggregation are cheap, transferable levers for state-space image models.

What carries the argument

The two load-bearing components are Nested S-shaped Scanning (NSS) and Sequence Shuffle Attention (SSA). NSS partitions the feature map into non-overlapping stripes and follows an S-shaped path both inside each stripe and across stripes, so a scan can be long-range while keeping neighboring pixels adjacent; the shift-stripe mechanism moves the stripe boundaries between consecutive modules so boundary pixels are not permanently separated. SSA takes the four SSO-processed sequences, spatially pools them, reshuffles the pooled channels so that the k-th channel of all four directions sits together, applies group convolution to produce attention weights per channel, unshuffles the weights, and uses them in a weighted sum of the four sequences. Together they convert the selective scan's 1D processing into a 2D-aware restore-then-fuse operation with no added scanning cost.

What would settle it

Run MaIR and the key ablations (a plain Z-shaped scan in place of NSS, and simple summation or gating in place of SSA) several times with different random seeds on Urban100 ×2 lightweight super-resolution. If the reported 0.03–0.12 dB margins fall inside the seed-to-seed variance, the central claim that NSS and SSA cause the improvement is not supported.

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Extended reading notes

Core claim

The paper's central claim is that the dominant Mamba-restoration pipeline — flatten a 2D feature map into rows and columns, run selective scans independently, then add the results — throws away exactly the structure that restoration needs. MaIR replaces the flattening with NSS: the feature map is cut into non-overlapping stripes, each stripe is traversed in an S-shaped path, and the paths connect across stripes, so pixels that are near in the image stay near in the 1D sequence even at stripe boundaries; a shift-stripe variant between successive modules moves the boundaries so no region is permanently split. It replaces the additive recombination with SSA, which pools each of the four directional sequences, shuffles the channels so corresponding channels from all directions are adjacent, computes per-channel attention weights with group convolution, and sums the four sequences using those weights. On 14 benchmark datasets covering super-resolution, denoising, deblurring, and dehazing, the paper reports that MaIR exceeds 40 baselines, with the largest margins on dehazing and consistent gains such as 0.03–0.12 dB over MambaIR on classic super-resolution. The message is that scan topology and sequence aggregation, not extra capacity, are what make a Mamba restoration model competitive.

Load-bearing premise

The results rest on the assumption that differences of roughly 0.03 to 0.12 dB in PSNR against strong baselines, reported without error bars or repeated runs, reflect a genuine improvement of the proposed scanning and aggregation rather than training noise.

Editorial extensions

If this is right

  • A Mamba restoration model can capture long-range dependencies without sacrificing 2D structure, so the near-linear complexity of the selective scan is retained.
  • The shift-stripe mechanism means stripe boundaries are handled by alternating module partitions, so seam artifacts should not accumulate in deep stacks.
  • SSA gives a learned, per-channel weight to each of the four scan directions, allowing the network to rely more on the direction that carries the most relevant context.
  • The same architecture, without task-specific changes beyond the loss function, reaches the top reported numbers on super-resolution, denoising, deblurring, and dehazing.

Reading between the lines

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

  • Because NSS and SSA add no trainable parameters to the scanning itself, the design should transfer to other Mamba vision tasks, such as low-light enhancement or compression artifact removal, where locality and continuity also matter.
  • SSA is effectively a learned mixture over scan directions; extending it to more than four directions, or to multi-scale stripes, is a natural next test that the paper does not run.
  • The paper's own ablations show the PSNR margin over a plain Z-shaped scan is small, so a perceptual study on high-texture regions would be a sharper test of the locality-and-continuity claim than aggregate scores.
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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 / 6 minor

Summary. The paper proposes MaIR, a Mamba-based image restoration network whose two main contributions are a Nested S-shaped Scanning strategy (NSS) and a Sequence Shuffle Attention block (SSA). NSS divides feature maps into stripes and scans them in an S-shaped path, with a shift-stripe mechanism across successive modules; SSA aggregates the four scanned sequences by computing channel-wise attention weights after a shuffle operation. The method is evaluated on super-resolution, denoising, deblurring, and dehazing, with comparisons to about forty baselines across fourteen datasets, and the paper claims state-of-the-art performance on all four tasks. The architecture is described clearly and the code is released.

Significance. If the claimed gains are robust, NSS and SSA are simple and largely architecture-agnostic modifications that could benefit Mamba-based restoration beyond this specific network. The paper's strengths are the breadth of the evaluation (four tasks, fourteen datasets), the clear description of the scanning and aggregation designs, and the public code release, which makes the results directly checkable. However, the empirical case is weakened by the absence of any uncertainty quantification: many reported improvements over strong baselines are just a few hundredths of a dB, and the ablation margins used to attribute the gains to NSS and SSA are of the same size as typical run-to-run variation. The significance therefore depends on whether the small margins are systematic rather than noise.

major comments (3)
  1. [Abstract; Tables 1 and 6] The claim that MaIR 'surpasses 40 baselines' and achieves state-of-the-art performance on all four tasks is not supported by the paper's own tables. In Table 1 (×4, Set5), MambaIR reports 33.03 dB PSNR while MaIR reports 32.93 dB; in Table 1 (×2, Set5), MambaIR reports 38.57 dB versus MaIR's 38.56 dB; and in Table 6 (SOTS-Indoor), UVM-Net reports 40.17 dB versus MaIR's 39.45 dB. The abstract and conclusion should be revised to state precisely where MaIR is best and to acknowledge the dataset-specific exceptions, rather than claiming a global state-of-the-art. The same issue appears in the intro's contribution bullet, which says MaIR 'obtains state-of-the-art performance on four tasks across 14 benchmarks.'
  2. [§4.5, Tables 7–9] The central attribution of the performance gain to NSS and SSA rests on ablation margins of 0.01–0.05 dB PSNR. Table 7 shows 32.97 vs 32.94 when replacing NSS with a Z-shaped scan, and 32.97 vs 32.93 when removing the shift-stripe mechanism; Table 8 shows 32.97 vs 32.90 when SSA is replaced by simple sequence-wise addition; Table 9 shows identical PSNR values of 32.97 for stripe widths 4, 8, and 16. No repeated seeds, error bars, confidence intervals, or significance tests are reported anywhere in the paper. Because these margins are comparable to typical run-to-run variance for lightweight super-resolution models, the current evidence does not rule out training noise as the source of the reported differences. Please provide multi-seed results with mean±std, paired significance tests, or a control experiment that changes only the shuffle indexing while preserving the permutation class, to demonstrate that the observed margins are systematic.
  3. [§2, §3.2, Conclusion; Table 2] The paper repeatedly describes NSS as 'cost-free' and concludes that MaIR improves image quality 'without introducing extra computations,' but this is contradicted by the reported MACs. Table 2 shows MaIR-Tiny using 207.8G MACs at ×2 versus 167.1G for MambaIR-Tiny, while MaIR-Small uses 542.0G versus 567.5G for MambaIR-Small. The efficiency claim should be clarified: the stripe reordering itself may be rearrangement-only, but SSA adds pooling, group convolution, and weighted summation, and the actual overhead should be reported for each configuration. As written, the 'cost-free' claim is misleading.
minor comments (6)
  1. [§3.2] 'shift-stipe mechanism' should be 'shift-stripe mechanism.'
  2. [§2.1] In the sentence listing CNN-based deblurring methods, the citation '[39, 39, 48, 60]' contains a duplicate entry; one occurrence of [39] should be removed.
  3. [§4] The experimental settings are deferred entirely to the supplementary materials. To make the main paper self-contained, please include the essential training details (number of RMGs and RMBs, patch size, optimizer, iterations, learning rate schedule, and hardware) in the main text or an appendix.
  4. [Figures 3 and 4] The diagrams for NSS and SSA are difficult to read at the printed size; the stripe boundaries, shuffle order, and group-convolution labels should be enlarged and clarified.
  5. [§3.1, Eq. (2)] The Charbonnier loss is typeset as 'L = p ∥y − y′∥2 + ϵ2'; the square-root symbol appears to be missing. Please correct the equation.
  6. [Abstract] The phrase 'surpasses 40 baselines across 14 challenging datasets' should specify whether the comparison is per-dataset best or an aggregate, since the paper's own tables contain non-winning entries.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: MaIR is an empirical architecture paper whose claims are tested against external benchmarks, with no fitted-input-as-prediction or load-bearing self-citation.

full rationale

This paper contains no analytic derivation that could be circular. The central claim—that NSS and SSA improve restoration—is supported by benchmarking on 14 datasets against 40 baselines, all external (e.g., MambaIR, SwinIR, Restormer), and by ablations that replace the proposed components with alternative scans and aggregations from other papers. The only overlapping-author citations (CLEARER [14], All-In-One [25], CODE [66]) appear as contextual related work or as a comparison baseline, not as justification for the paper's design choices. No parameter is fitted to a subset of data and then 'predicted' on a dependent quantity; the reported PSNR/SSIM values are held-out test-set results from standard pipelines. The small ablation margins (0.03–0.07 dB on Urban100) raise statistical-reliability concerns, and the SOTA claim is partially contradicted by the paper's own tables (e.g., MambaIR at x4 on Set5 reports 33.03 dB vs MaIR's 32.93 dB), but these are issues of evidence strength and selective reporting, not circularity. Under the stated rubric, no circular step can be exhibited, so the score is 0.

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

The paper rests on standard empirical assumptions: that Mamba's SSO is a good sequence model for images, that the chosen benchmarks and metrics reflect restoration quality, and that the adopted MambaIR-style backbone is a strong base. The design hyperparameters (stripe width, number of directions, loss epsilon) are standard and are investigated, not fitted to the evaluation metric, so circularity is low.

free parameters (4)
  • stripe width ws = 8 (default, tested 2-32)
    Width of non-overlapping stripes in NSS. The paper studies ws = 2,4,8,16,32 and shows robust performance (PSNR 32.92-32.97 on Urban100 x2). The default is used in main experiments, stated in Section 3.2.
  • shift-stripe widths = ws/2 for first and last stripes, ws for others
    Set in Section 3.2 as part of the shift-stripe mechanism to cover boundary regions between adjacent stripes.
  • number of scanning directions K = 4
    The method flattens features along four directions (forward/backward rows and columns), following prior work [18,31]. This is a design choice inherited from Vmamba/MambaIR.
  • Charbonnier loss epsilon = 1e-3
    Hyper-parameter in the loss for denoising, deblurring, dehazing (Section 3.1), set empirically as in prior works.
assumptions (4)
  • domain assumption Mamba's selective scan operation (SSO) captures long-range dependencies in 1D sequences efficiently.
    The entire approach relies on SSO as the core sequence model (Sections 2.2 and 3.2). This is an established but unproved foundation in the paper.
  • domain assumption PSNR/SSIM on standard benchmarks are valid proxies for image restoration quality.
    All comparisons and SOTA claims rest on these metrics (Section 4). The paper does not include perceptual metrics or human studies.
  • domain assumption The degradation models used (bicubic downsampling, Gaussian noise, GoPro blur, RESIDE haze) are representative of real restoration tasks.
    Training and test sets follow prior work (Sections 4.1-4.4). The paper does not test on real-world degradations except the SIDD real denoising benchmark.
  • ad hoc to paper The MambaIR-style backbone (Residual Mamba Groups and Blocks) is an effective base architecture.
    MaIR adopts the architecture of MambaIR with only scanning and aggregation changes, assuming the backbone is a strong baseline (Section 3.1).

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Cite this review

Pith. "Pith review of MaIR: A Locality- and Continuity-Preserving Mamba for Image Restoration." pith.science (2026). https://pith.science/paper/CJ2HWH2C

@misc{pith2026241220066,
  author       = {Pith},
  title        = {Pith review of: MaIR: A Locality- and Continuity-Preserving Mamba for Image Restoration},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CJ2HWH2C}},
  note         = {Machine review of arXiv:2412.20066}
}
read the original abstract

Recent advancements in Mamba have shown promising results in image restoration. These methods typically flatten 2D images into multiple distinct 1D sequences along rows and columns, process each sequence independently using selective scan operation, and recombine them to form the outputs. However, such a paradigm overlooks two vital aspects: i) the local relationships and spatial continuity inherent in natural images, and ii) the discrepancies among sequences unfolded through totally different ways. To overcome the drawbacks, we explore two problems in Mamba-based restoration methods: i) how to design a scanning strategy preserving both locality and continuity while facilitating restoration, and ii) how to aggregate the distinct sequences unfolded in totally different ways. To address these problems, we propose a novel Mamba-based Image Restoration model (MaIR), which consists of Nested S-shaped Scanning strategy (NSS) and Sequence Shuffle Attention block (SSA). Specifically, NSS preserves locality and continuity of the input images through the stripe-based scanning region and the S-shaped scanning path, respectively. SSA aggregates sequences through calculating attention weights within the corresponding channels of different sequences. Thanks to NSS and SSA, MaIR surpasses 40 baselines across 14 challenging datasets, achieving state-of-the-art performance on the tasks of image super-resolution, denoising, deblurring and dehazing. The code is available at https://github.com/XLearning-SCU/2025-CVPR-MaIR.

Figures

Figures reproduced from arXiv: 2412.20066 by the authors.

Figure 1
Figure 1. The scanning strategies in existing Mamba-based meth [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Illustrations of MaIR. (a) The overall architecture of MaIR, highlighting its core component, Residual Mamba Group (RMG). [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Illustrations of (a) Nested S-shaped Scanning strategy [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Illustration of the Sequence Shuffle Attention (SSA). The input features [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]
Figure 5
Figure 5. Figure 5: Visual comparison of ×4 image super-resolution results on the Manga109 dataset. MaIR demonstrates superior visual quality, particularly in preserving fine details and textures. 5 [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]
Figure 6
Figure 6. Figure 6: Visual comparison of image denoising results on the Urban100 dataset. MaIR effectively removes noise in the images and [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 7
Figure 7. Figure 7: Visual comparison of motion deblurring results on the GoPro dataset. MaIR demonstrates superior performance in effectively [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]
Figure 8
Figure 8. Figure 8: Visual comparison of image dehazing results on the SOTS dataset. MaIR can effectively remove haze and restore content with [PITH_FULL_IMAGE:figures/full_fig_p009_8.png]
Figure 9
Figure 9. Figure 9: Visual comparisons of different scanning strategies, illustrating that i) windows-based scanning path overlooks the continuity [PITH_FULL_IMAGE:figures/full_fig_p010_9.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

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