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arxiv: 2605.25892 · v1 · pith:WCTY47T7new · submitted 2026-05-25 · 💻 cs.CV

SP-MoMamba: Superpixel-driven Mixture of State Space Experts for Efficient Image Super-Resolution

Wenbin Zou , Yawen Cui , Yi Wang , Lap-Pui Chau , Liang Chen , Jinshan Pan , Huiping Zhuang , Guanbin Li This is my paper

Pith reviewed 2026-06-29 22:47 UTC · model grok-4.3

classification 💻 cs.CV
keywords image super-resolutionstate space modelssuperpixelsmixture of expertsefficient SRsemantic groupingMamba architecture
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The pith

Superpixel units replace rigid scanning in state space models to maintain topology during image super-resolution.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper sets out to show that superpixels can serve as the basic processing units for state space models in single-image super-resolution, turning fixed-grid reshaping into semantic grouping that keeps spatial relationships intact. This change is meant to cut down on the artifacts that come from data-agnostic scanning while still allowing long-range modeling at linear cost. The design adds a routing system that picks the right expert scale for each superpixel group and a separate local module to keep fine edges. If these pieces work as described, the result is higher-fidelity outputs on standard test sets together with better speed versus quality balance than earlier efficient SR approaches.

Core claim

SP-MoMamba converts traditional rigid scanning of 2D images into semantic-level interaction by treating superpixels as fundamental units; the SP-SSM compresses homogeneous regions into high-order tokens to keep global topology, the MSS-MoE applies dynamic routing to assign scale-specific experts that match varying semantic sizes, and the LSME restores high-frequency details that global abstraction tends to lose.

What carries the argument

Superpixel-driven State Space Model (SP-SSM) paired with Multi-Scale Superpixel Mixture of State Space Experts (MSS-MoE) that routes tokens by semantic granularity.

If this is right

  • Reconstruction quality on standard SR benchmarks exceeds that of prior efficient methods.
  • Computational cost drops relative to performance because scale-specific experts avoid redundant work.
  • Multi-scale textures are captured without breaking global consistency across the output image.
  • High-frequency edges and fine structures remain sharp after the global modeling step.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same superpixel routing could be tried on other dense tasks such as denoising or semantic segmentation to test whether semantic units generalize beyond super-resolution.
  • If superpixel tokens reduce the need for very deep stacks, the approach might allow smaller overall models while keeping accuracy.
  • Running the method on real camera noise rather than clean benchmarks would show whether the grouping step helps or hurts under imperfect input conditions.

Load-bearing premise

Grouping pixels into superpixels will keep overall image structure intact and avoid both new artifacts and loss of sharp detail.

What would settle it

A side-by-side test on a benchmark image set showing lower PSNR or visible boundary artifacts in regions with fine texture compared with a rigid-scanning baseline would disprove the claim.

Figures

Figures reproduced from arXiv: 2605.25892 by Guanbin Li, Huiping Zhuang, Jinshan Pan, Lap-Pui Chau, Liang Chen, Wenbin Zou, Yawen Cui, Yi Wang.

Figure 1
Figure 1. Figure 1: (a) The existing method [11] suffers from the ad [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Analysis semantic preservation capability of different methods. The larger the Diffusion Index (DI), the more semantically [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Architecture of SP-MoMamba, built upon stacked Layers of Experts (LoEs) that hierarchically couple two complementary [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 2
Figure 2. Figure 2: It can be clearly observed that when reconstructing [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: Illustration of the superpixel-driven state space model (SP-SSM). One-hot mask should be [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Superpixel sampling of our method, which initializes [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Qualitative comparison of our SP-MoMamba-T with state-of-the-art ultra-lightweight methods on [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Qualitative comparison of our SP-MoMamba-B with state-of-the-art methods on [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Visual comparison of Local Attribution Maps (LAM) [43] and Diffusion Indices (DI). The red dot indicates the target [PITH_FULL_IMAGE:figures/full_fig_p010_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Qualitative comparisons with existing efficient SR methods on the real-world test set RealSRv3 [61]. (Zoom in for the [PITH_FULL_IMAGE:figures/full_fig_p010_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Comparison between performance vs Inference times [PITH_FULL_IMAGE:figures/full_fig_p011_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Ablation studies of our SP-MoMamba-T at scale factors of ×4 on BSD100 [56] test set. (a) represents ablation [PITH_FULL_IMAGE:figures/full_fig_p012_11.png] view at source ↗
Figure 13
Figure 13. Figure 13: Analysis of gating mechanism on SP-SSM. We [PITH_FULL_IMAGE:figures/full_fig_p013_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Expert routing analysis. (a) We plot the decisions made by the routing function for SP-MoMamba-T over the depth [PITH_FULL_IMAGE:figures/full_fig_p014_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Qualitative comparisons with existing LLIE methods on the real-world test set LOLv1 and LOLv2-Real [82]. [PITH_FULL_IMAGE:figures/full_fig_p014_15.png] view at source ↗
read the original abstract

State space models (SSMs) have emerged as a powerful paradigm for efficient single-image super-resolution (SR) due to their linear complexity and long-range modeling capabilities. However, existing Mamba-based methods typically rely on data-agnostic rigid scanning, which reshapes 2D images into 1D sequences over a fixed grid, inevitably disrupting spatial-semantic topology and introducing artifacts. Inspired by the \textbf{Gestalt perceptual grouping theory}, we propose \textbf{SP-MoMamba}, a superpixel-driven mixture of state space experts designed for content-aware SR. Our core idea is to transform the traditional rigid scanning into a \textbf{semantic-level interaction} by treating superpixels as fundamental units. Specifically, we introduce the \textbf{Superpixel-driven State Space Model (SP-SSM)}, which compresses semantically homogeneous regions into high-order tokens to preserve global topological consistency. To address the conflict between fixed scanning scales and diverse semantic granularities, we develop the \textbf{Multi-Scale Superpixel Mixture of State Space Experts (MSS-MoE)}. This module utilizes a dynamic routing mechanism to adaptively assign scale-specific experts, effectively capturing multi-scale textures while reducing computational redundancy. Furthermore, to prevent the loss of high-frequency details during global abstraction, we introduce a \textbf{Local Spatial Modulation Expert (LSME)} to complement the global modeling, ensuring a precise reconstruction of sharp edges and fine structures. Extensive experiments on standard benchmarks demonstrate that SP-MoMamba achieves superior reconstruction fidelity and a more favorable efficiency-performance trade-off compared to state-of-the-art efficient SR methods.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces SP-MoMamba, a superpixel-driven mixture-of-experts architecture for efficient single-image super-resolution. It replaces rigid-grid scanning in Mamba-based SR with the Superpixel-driven State Space Model (SP-SSM) that compresses homogeneous regions into high-order tokens, the Multi-Scale Superpixel Mixture of State Space Experts (MSS-MoE) that routes scale-specific experts via dynamic gating, and the Local Spatial Modulation Expert (LSME) that restores high-frequency details. The central claim is that this content-aware design preserves global topological consistency, captures multi-scale textures without redundancy, and yields superior reconstruction fidelity together with a better efficiency-performance trade-off than prior efficient SR methods on standard benchmarks.

Significance. If the experimental claims are substantiated, the work offers a concrete route to make state-space models more semantically adaptive in vision tasks. By grounding tokenization in superpixel segmentation rather than fixed grids, it directly targets a documented weakness of existing Mamba SR pipelines. The combination of SP-SSM, scale-aware MoE routing, and a complementary local expert is a coherent architectural response to the tension between global modeling and local fidelity; successful validation would supply a reusable template for other dense-prediction tasks that currently suffer from topology disruption under 1-D sequence models.

major comments (2)
  1. [§3.2] §3.2 (SP-SSM): The claim that superpixel tokens 'preserve global topological consistency' is load-bearing for the fidelity advantage, yet the manuscript does not specify the serialization order used to feed the 1-D SSM. If the ordering is a fixed raster scan or arbitrary flattening rather than an adjacency-preserving traversal (e.g., graph-based or space-filling curve respecting superpixel boundaries), the topology-preservation argument collapses and the method reintroduces the spatial-semantic disruption it criticizes in rigid-grid baselines.
  2. [§4] §4 (Experiments): The abstract asserts 'superior reconstruction fidelity' and 'more favorable efficiency-performance trade-off,' but the provided text supplies neither quantitative tables, baseline comparisons, nor error bars. Without these data the central empirical claim cannot be evaluated; the reader is left unable to verify whether the topology-preserving mechanism actually delivers measurable gains.
minor comments (2)
  1. [§3.3] Notation for the routing weights in MSS-MoE is introduced without an explicit equation; adding a compact definition (e.g., Eq. (X)) would improve reproducibility.
  2. [Figure 2] Figure 2 caption refers to 'scale-specific experts' but the legend does not label the individual expert branches; this reduces clarity of the multi-scale routing diagram.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on SP-MoMamba. The comments highlight important areas for clarification in the architectural description and experimental presentation. We address each point below and commit to revisions that strengthen the manuscript without altering its core claims.

read point-by-point responses

  1. Referee: [§3.2] §3.2 (SP-SSM): The claim that superpixel tokens 'preserve global topological consistency' is load-bearing for the fidelity advantage, yet the manuscript does not specify the serialization order used to feed the 1-D SSM. If the ordering is a fixed raster scan or arbitrary flattening rather than an adjacency-preserving traversal (e.g., graph-based or space-filling curve respecting superpixel boundaries), the topology-preservation argument collapses and the method reintroduces the spatial-semantic disruption it criticizes in rigid-grid baselines.

    Authors: We agree that the serialization order is essential to substantiate the topology-preservation claim. In the revised manuscript we will explicitly describe in §3.2 that superpixel tokens are serialized via an adjacency-preserving graph traversal (constructed from superpixel boundary adjacency) before being fed to the 1-D SSM. This ordering respects semantic boundaries and directly supports the Gestalt-inspired design; the revision will include a short diagram or pseudocode for clarity. revision: yes

  2. Referee: [§4] §4 (Experiments): The abstract asserts 'superior reconstruction fidelity' and 'more favorable efficiency-performance trade-off,' but the provided text supplies neither quantitative tables, baseline comparisons, nor error bars. Without these data the central empirical claim cannot be evaluated; the reader is left unable to verify whether the topology-preserving mechanism actually delivers measurable gains.

    Authors: The full manuscript contains an experiments section (§4) with the requested quantitative results. To address the concern that these were not sufficiently visible, we will revise §4 to ensure all tables (PSNR/SSIM on standard benchmarks, runtime/FLOPs comparisons, and error bars from multiple runs) are presented with clear captions and are cross-referenced from the abstract. No new experiments are required; the existing data will be formatted for immediate verifiability. revision: yes

Circularity Check

0 steps flagged

No circularity: architecture proposal with independent empirical claims

full rationale

The paper introduces a new architecture (SP-SSM, MSS-MoE, LSME) motivated by Gestalt theory and critiques of rigid scanning in prior Mamba SR methods. No equations, fitted parameters renamed as predictions, or self-citation chains appear in the provided text. Central claims rest on the proposed design choices and external benchmark experiments rather than any reduction to inputs by construction. The topology-preservation argument is a design hypothesis, not a self-referential derivation.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Abstract-only review yields no explicit free parameters or invented physical entities; the central design rests on the domain assumption that Gestalt perceptual grouping improves SSM scanning for SR.

axioms (1)
  • domain assumption Gestalt perceptual grouping theory can be applied to transform rigid image scanning into semantic-level interaction for super-resolution
    Explicitly stated as inspiration for the core idea in the abstract

pith-pipeline@v0.9.1-grok · 5843 in / 1089 out tokens · 39741 ms · 2026-06-29T22:47:53.993718+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

85 extracted references · 17 canonical work pages · 8 internal anchors

  1. [1]

    Image super-resolution using deep convolutional networks,

    C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,”IEEE transactions on pattern analysis and machine intelligence, vol. 38, no. 2, pp. 295–307, 2015. JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, APRIL 2026 16

    2015

  2. [2]

    Enhanced deep residual networks for single image super-resolution,

    B. Lim, S. Son, H. Kim, S. Nah, and K. M. Lee, “Enhanced deep residual networks for single image super-resolution,” in2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2017, pp. 1132–1140

    2017

  3. [3]

    Image super- resolution using very deep residual channel attention networks,

    Y . Zhang, K. Li, K. Li, L. Wang, B. Zhong, and Y . Fu, “Image super- resolution using very deep residual channel attention networks,” in Proceedings of the European conference on computer vision (ECCV), 2018, pp. 286–301

    2018

  4. [4]

    Swinir: Image restoration using swin transformer,

    J. Liang, J. Cao, G. Sun, K. Zhang, L. Van Gool, and R. Timofte, “Swinir: Image restoration using swin transformer,” inProceedings of the IEEE/CVF international conference on computer vision, 2021, pp. 1833–1844

    2021

  5. [5]

    Efficient and explicit modelling of image hierarchies for image restoration,

    Y . Li, Y . Fan, X. Xiang, D. Demandolx, R. Ranjan, R. Timofte, and L. Van Gool, “Efficient and explicit modelling of image hierarchies for image restoration,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2023, pp. 18 278–18 289

    2023

  6. [6]

    Srformer: Permuted self-attention for single image super-resolution,

    Y . Zhou, Z. Li, C.-L. Guo, S. Bai, M.-M. Cheng, and Q. Hou, “Srformer: Permuted self-attention for single image super-resolution,” inProceedings of the IEEE/CVF International Conference on Computer Vision, 2023, pp. 12 780–12 791

    2023

  7. [7]

    Fast, accurate and lightweight super-resolution with neural architecture search,

    X. Chu, B. Zhang, H. Ma, R. Xu, and Q. Li, “Fast, accurate and lightweight super-resolution with neural architecture search,” in2020 25th International conference on pattern recognition (ICPR). IEEE, 2021, pp. 59–64

    2021

  8. [8]

    Image super-resolution via deep recursive residual network,

    Y . Tai, J. Yang, and X. Liu, “Image super-resolution via deep recursive residual network,” inProceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 3147–3155

    2017

  9. [9]

    Residual feature distillation network for lightweight image super-resolution,

    J. Liu, J. Tang, and G. Wu, “Residual feature distillation network for lightweight image super-resolution,” inComputer vision–ECCV 2020 workshops: Glasgow, UK, August 23–28, 2020, proceedings, part III

    2020

  10. [10]

    Springer, 2020, pp. 41–55

    2020

  11. [11]

    Fast and accurate single image super- resolution via information distillation network,

    Z. Hui, X. Wang, and X. Gao, “Fast and accurate single image super- resolution via information distillation network,” inProceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 723–731

    2018

  12. [12]

    Mambair: A simple baseline for image restoration with state-space model,

    H. Guo, J. Li, T. Dai, Z. Ouyang, X. Ren, and S.-T. Xia, “Mambair: A simple baseline for image restoration with state-space model,” in European conference on computer vision. Springer, 2024, pp. 222– 241

    2024

  13. [13]

    Mamba: Linear-Time Sequence Modeling with Selective State Spaces

    A. Gu and T. Dao, “Mamba: Linear-time sequence modeling with selective state spaces,”arXiv preprint arXiv:2312.00752, 2023

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  14. [14]

    VMamba: Visual State Space Model

    Y . Liu, Y . Tian, Y . Zhao, H. Yu, L. Xie, Y . Wang, Q. Ye, and Y . Liu, “Vmamba: Visual state space model,”arXiv preprint arXiv:2401.10166, 2024

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  15. [15]

    Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

    L. Zhu, B. Liao, Q. Zhang, X. Wang, W. Liu, and X. Wang, “Vision mamba: Efficient visual representation learning with bidirectional state space model,”arXiv preprint arXiv:2401.09417, 2024

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  16. [16]

    V oxel mamba: Group-free state space models for point cloud based 3d object detection,

    G. Zhang, L. Fan, C. He, Z. Lei, Z.-X. ZHANG, and L. Zhang, “V oxel mamba: Group-free state space models for point cloud based 3d object detection,”Advances in Neural Information Processing Systems, vol. 37, pp. 81 489–81 509, 2025

    2025

  17. [17]

    Mamba yolo: Ssms-based yolo for object detection,

    Z. Wang, C. Li, H. Xu, and X. Zhu, “Mamba yolo: Ssms-based yolo for object detection,”arXiv preprint arXiv:2406.05835, 2024

    work page arXiv 2024

  18. [18]

    Hi-mamba: Hierarchical mamba for efficient image super-resolution,

    J. Qiao, J. Liao, W. Li, Y . Zhang, Y . Guo, Y . Wen, Z. Qiu, J. Xie, J. Hu, and S. Lin, “Hi-mamba: Hierarchical mamba for efficient image super-resolution,”arXiv preprint arXiv:2410.10140, 2024

    work page arXiv 2024

  19. [19]

    Wave-mamba: Wavelet state space model for ultra-high-definition low-light image enhancement,

    W. Zou, H. Gao, W. Yang, and T. Liu, “Wave-mamba: Wavelet state space model for ultra-high-definition low-light image enhancement,” in Proceedings of the 32nd ACM International Conference on Multimedia, 2024, pp. 1534–1543

    2024

  20. [20]

    Freqmamba: Viewing mamba from a frequency perspective for image deraining,

    Z. Zhen, Y . Hu, and Z. Feng, “Freqmamba: Viewing mamba from a frequency perspective for image deraining,”arXiv preprint arXiv:2404.09476, 2024

    work page arXiv 2024

  21. [21]

    Mambairv2: Attentive state space restoration,

    H. Guo, Y . Guo, Y . Zha, Y . Zhang, W. Li, T. Dai, S.-T. Xia, and Y . Li, “Mambairv2: Attentive state space restoration,” inProceedings of the Computer Vision and Pattern Recognition Conference, 2025, pp. 28 124– 28 133

    2025

  22. [22]

    Efficient visual state space model for image deblurring,

    L. Kong, J. Dong, J. Tang, M.-H. Yang, and J. Pan, “Efficient visual state space model for image deblurring,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2025, pp. 12 710–12 719

    2025

  23. [23]

    A century of gestalt psychology in vi- sual perception: I. perceptual grouping and figure–ground organization

    J. Wagemans, J. H. Elder, M. Kubovy, S. E. Palmer, M. A. Peterson, M. Singh, and R. V on der Heydt, “A century of gestalt psychology in vi- sual perception: I. perceptual grouping and figure–ground organization.” Psychological bulletin, vol. 138, no. 6, p. 1172, 2012

    2012

  24. [24]

    Real-time single image and video super- resolution using an efficient sub-pixel convolutional neural network,

    W. Shi, J. Caballero, F. Husz ´ar, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert, and Z. Wang, “Real-time single image and video super- resolution using an efficient sub-pixel convolutional neural network,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 1874–1883

    2016

  25. [25]

    Joint wavelet sub-bands guided network for single image super-resolution,

    W. Zou, L. Chen, Y . Wu, Y . Zhang, Y . Xu, and J. Shao, “Joint wavelet sub-bands guided network for single image super-resolution,”IEEE Transactions on Multimedia, vol. 25, pp. 4623–4637, 2022

    2022

  26. [26]

    Accurate image super-resolution using very deep convolutional networks,

    J. Kim, J. K. Lee, and K. M. Lee, “Accurate image super-resolution using very deep convolutional networks,” in2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 1646– 1654

    2016

  27. [27]

    Swin transformer: Hierarchical vision transformer using shifted windows,

    Z. Liu, Y . Lin, Y . Cao, H. Hu, Y . Wei, Z. Zhang, S. Lin, and B. Guo, “Swin transformer: Hierarchical vision transformer using shifted windows,” inProceedings of the IEEE/CVF international conference on computer vision, 2021, pp. 10 012–10 022

    2021

  28. [28]

    Accurate image restoration with attention retractable transformer,

    J. Zhang, Y . Zhang, J. Gu, Y . Zhang, L. Kong, and X. Yuan, “Accurate image restoration with attention retractable transformer,”arXiv preprint arXiv:2210.01427, 2022

    work page arXiv 2022

  29. [29]

    Omni aggregation networks for lightweight image super-resolution,

    H. Wang, X. Chen, B. Ni, Y . Liu, and J. Liu, “Omni aggregation networks for lightweight image super-resolution,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 22 378–22 387

    2023

  30. [30]

    Fast, accurate, and lightweight super-resolution with cascading residual network,

    N. Ahn, B. Kang, and K.-A. Sohn, “Fast, accurate, and lightweight super-resolution with cascading residual network,” inProceedings of the European conference on computer vision (ECCV), 2018, pp. 252–268

    2018

  31. [31]

    Spatially-adaptive feature modulation for efficient image super-resolution,

    L. Sun, J. Dong, J. Tang, and J. Pan, “Spatially-adaptive feature modulation for efficient image super-resolution,” inProceedings of the IEEE/CVF international conference on computer vision, 2023, pp. 13 190–13 199

    2023

  32. [32]

    Efficient long-range attention network for image super-resolution,

    X. Zhang, H. Zeng, S. Guo, and L. Zhang, “Efficient long-range attention network for image super-resolution,” inEuropean conference on computer vision. Springer, 2022, pp. 649–667

    2022

  33. [33]

    Transformer for single image super-resolution,

    Z. Lu, J. Li, H. Liu, C. Huang, L. Zhang, and T. Zeng, “Transformer for single image super-resolution,” inProceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022, pp. 457– 466

    2022

  34. [34]

    Self-calibrated efficient transformer for lightweight super-resolution,

    W. Zou, T. Ye, W. Zheng, Y . Zhang, L. Chen, and Y . Wu, “Self-calibrated efficient transformer for lightweight super-resolution,” inProceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022, pp. 930–939

    2022

  35. [35]

    Lightweight image super- resolution with superpixel token interaction,

    A. Zhang, W. Ren, Y . Liu, and X. Cao, “Lightweight image super- resolution with superpixel token interaction,” inProceedings of the IEEE/CVF international conference on computer vision, 2023, pp. 12 728–12 737

    2023

  36. [36]

    Scan clusters, not pixels: A cluster-centric paradigm for efficient ultra-high-definition image restoration,

    C. Wu, L. Wang, Z. Zheng, Y . Cui, Z. Yang, X. Chen, Y . Zhang, W. Jiang, and J. Xia, “Scan clusters, not pixels: A cluster-centric paradigm for efficient ultra-high-definition image restoration,”arXiv preprint arXiv:2602.21917, 2026

    work page arXiv 2026

  37. [37]

    Scaling vision with sparse mixture of experts,

    C. Riquelme, J. Puigcerver, B. Mustafa, M. Neumann, R. Jenatton, A. Susano Pinto, D. Keysers, and N. Houlsby, “Scaling vision with sparse mixture of experts,”Advances in Neural Information Processing Systems, vol. 34, pp. 8583–8595, 2021

    2021

  38. [38]

    Residual mixture of experts,

    L. Wu, M. Liu, Y . Chen, D. Chen, X. Dai, and L. Yuan, “Residual mixture of experts,”arXiv preprint arXiv:2204.09636, 2022

    work page arXiv 2022

  39. [39]

    Moesr: Blind super-resolution using kernel-aware mixture of experts,

    M. Emad, M. Peemen, and H. Corporaal, “Moesr: Blind super-resolution using kernel-aware mixture of experts,” inProceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2022, pp. 3408– 3417

    2022

  40. [40]

    See more details: Efficient image super-resolution by experts mining,

    E. Zamfir, Z. Wu, N. Mehta, Y . Zhang, and R. Timofte, “See more details: Efficient image super-resolution by experts mining,” inForty- first International Conference on Machine Learning, 2024

    2024

  41. [41]

    Swin2- mose: A new single image supersolution model for remote sensing,

    L. Rossi, V . Bernuzzi, T. Fontanini, M. Bertozzi, and A. Prati, “Swin2- mose: A new single image supersolution model for remote sensing,”IET Image Processing, vol. 19, no. 1, p. e13303, 2025

    2025

  42. [42]

    Efficient and degradation-adaptive network for real-world image super-resolution,

    J. Liang, H. Zeng, and L. Zhang, “Efficient and degradation-adaptive network for real-world image super-resolution,” inEuropean Conference on Computer Vision. Springer, 2022, pp. 574–591

    2022

  43. [43]

    Swin2sr: Swinv2 transformer for compressed image super-resolution and restoration,

    M. V . Conde, U.-J. Choi, M. Burchi, and R. Timofte, “Swin2sr: Swinv2 transformer for compressed image super-resolution and restoration,” in European Conference on Computer Vision. Springer, 2022, pp. 669– 687

    2022

  44. [44]

    Interpreting super-resolution networks with local attribution maps,

    J. Gu and C. Dong, “Interpreting super-resolution networks with local attribution maps,” inProceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2021, pp. 9199–9208

    2021

  45. [45]

    Dual aggregation transformer for image super-resolution,

    Z. Chen, Y . Zhang, J. Gu, L. Kong, X. Yang, and F. Yu, “Dual aggregation transformer for image super-resolution,” inProceedings of JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, APRIL 2026 17 the IEEE/CVF international conference on computer vision, 2023, pp. 12 312–12 321

    2026

  46. [46]

    GLU Variants Improve Transformer

    N. Shazeer, “Glu variants improve transformer,”arXiv preprint arXiv:2002.05202, 2020

    work page internal anchor Pith review Pith/arXiv arXiv 2002

  47. [47]

    Categorical Reparameterization with Gumbel-Softmax

    E. Jang, S. Gu, and B. Poole, “Categorical reparameterization with gumbel-softmax,”arXiv preprint arXiv:1611.01144, 2016

    work page internal anchor Pith review Pith/arXiv arXiv 2016

  48. [48]

    Superpixel sampling networks,

    V . Jampani, D. Sun, M.-Y . Liu, M.-H. Yang, and J. Kautz, “Superpixel sampling networks,” inProceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 352–368

    2018

  49. [49]

    Efficient image super-resolution using pixel attention,

    H. Zhao, X. Kong, J. He, Y . Qiao, and C. Dong, “Efficient image super-resolution using pixel attention,” inComputer Vision–ECCV 2020 Workshops: Glasgow, UK, August 23–28, 2020, Proceedings, Part III

    2020

  50. [50]

    Springer, 2020, pp. 56–72

    2020

  51. [51]

    A dynamic residual self-attention network for lightweight single image super-resolution,

    K. Park, J. W. Soh, and N. I. Cho, “A dynamic residual self-attention network for lightweight single image super-resolution,”IEEE Transac- tions on Multimedia, vol. 25, pp. 907–918, 2021

    2021

  52. [52]

    Feature enhanced cascading attention network for lightweight image super-resolution,

    F. Huang, H. Liu, L. Chen, Y . Shen, and M. Yu, “Feature enhanced cascading attention network for lightweight image super-resolution,” Scientific Reports, vol. 15, no. 1, p. 2051, 2025

    2051

  53. [53]

    Srconvnet: A transformer-style convnet for lightweight image super-resolution,

    F. Li, R. Cong, J. Wu, H. Bai, M. Wang, and Y . Zhao, “Srconvnet: A transformer-style convnet for lightweight image super-resolution,” International Journal of Computer Vision, vol. 133, no. 1, pp. 173–189, 2025

    2025

  54. [54]

    Transforming image super- resolution: A convformer-based efficient approach,

    G. Wu, J. Jiang, J. Jiang, and X. Liu, “Transforming image super- resolution: A convformer-based efficient approach,”IEEE Transactions on Image Processing, 2024

    2024

  55. [55]

    Ntire 2017 challenge on single image super-resolution: Methods and results,

    R. Timofte, E. Agustsson, L. V . Gool, M. Yang, L. Zhang, B. Lim, S. Son, H. Kim, S. Nah, and K. M. L. et al., “Ntire 2017 challenge on single image super-resolution: Methods and results,” in2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2017, pp. 1110–1121

    2017

  56. [56]

    Low- complexity single-image super-resolution based on nonnegative neighbor embedding,

    M. Bevilacqua, A. Roumy, C. Guillemot, and M. L. Alberi-Morel, “Low- complexity single-image super-resolution based on nonnegative neighbor embedding,” 2012

    2012

  57. [57]

    On single image scale-up using sparse-representations,

    R. Zeyde, M. Elad, and M. Protter, “On single image scale-up using sparse-representations,” 2010

    2010

  58. [58]

    A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics,

    D. Martin, C. Fowlkes, D. Tal, and J. Malik, “A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics,” inProceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, vol. 2, 2001, pp. 416–423 vol.2

    2001

  59. [59]

    Single image super-resolution from transformed self-exemplars,

    J. Huang, A. Singh, and N. Ahuja, “Single image super-resolution from transformed self-exemplars,” in2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 5197–5206

    2015

  60. [60]

    Sketch- based manga retrieval using manga109 dataset,

    Y . Matsui, K. Ito, Y . Aramaki, T. Yamasaki, and K. Aizawa, “Sketch- based manga retrieval using manga109 dataset,” 2015

    2015

  61. [61]

    Shufflemixer: An efficient convnet for image super-resolution,

    L. Sun, J. Pan, and J. Tang, “Shufflemixer: An efficient convnet for image super-resolution,”Advances in Neural Information Processing Systems, vol. 35, pp. 17 314–17 326, 2022

    2022

  62. [62]

    Adam: A Method for Stochastic Optimization

    D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980, 2014

    work page internal anchor Pith review Pith/arXiv arXiv 2014

  63. [63]

    Toward real-world single image super-resolution: A new benchmark and a new model,

    J. Cai, H. Zeng, H. Yong, Z. Cao, and L. Zhang, “Toward real-world single image super-resolution: A new benchmark and a new model,” inProceedings of the IEEE/CVF international conference on computer vision, 2019, pp. 3086–3095

    2019

  64. [64]

    Camixersr: Only details need more

    Y . Wang, Y . Liu, S. Zhao, J. Li, and L. Zhang, “Camixersr: Only details need more” attention”,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 25 837–25 846

    2024

  65. [65]

    Exploring frequency-inspired optimization in transformer for efficient single image super-resolution,

    A. Li, L. Zhang, Y . Liu, and C. Zhu, “Exploring frequency-inspired optimization in transformer for efficient single image super-resolution,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025

    2025

  66. [66]

    Dual-domain modulation network for lightweight image super-resolution,

    W. Li, H. Guo, Y . Hou, G. Gao, and Z. Ma, “Dual-domain modulation network for lightweight image super-resolution,”IEEE Transactions on Multimedia, pp. 1–11, 2026

    2026

  67. [67]

    Slic superpixels compared to state-of-the-art superpixel methods,

    R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. S ¨usstrunk, “Slic superpixels compared to state-of-the-art superpixel methods,”IEEE transactions on pattern analysis and machine intelligence, vol. 34, no. 11, pp. 2274–2282, 2012

    2012

  68. [68]

    Vision transformer with super token sampling,

    H. Huang, X. Zhou, J. Cao, R. He, and T. Tan, “Vision transformer with super token sampling,”arXiv preprint arXiv:2211.11167, 2022

    work page arXiv 2022

  69. [69]

    Embedding fourier for ultra-high-definition low-light image enhance- ment,

    C. Li, C.-L. Guo, M. Zhou, Z. Liang, S. Zhou, R. Feng, and C. C. Loy, “Embedding fourier for ultra-high-definition low-light image enhance- ment,”arXiv preprint arXiv:2302.11831, 2023

    work page arXiv 2023

  70. [70]

    Retinex- former: One-stage retinex-based transformer for low-light image en- hancement,

    Y . Cai, H. Bian, J. Lin, H. Wang, R. Timofte, and Y . Zhang, “Retinex- former: One-stage retinex-based transformer for low-light image en- hancement,” inProceedings of the IEEE/CVF international conference on computer vision, 2023, pp. 12 504–12 513

    2023

  71. [71]

    Dmfourllie: dual-stage and multi-branch fourier network for low-light image enhancement,

    T. Zhang, P. Liu, M. Zhao, and H. Lv, “Dmfourllie: dual-stage and multi-branch fourier network for low-light image enhancement,” in Proceedings of the 32nd ACM International Conference on Multimedia, 2024, pp. 7434–7443

    2024

  72. [72]

    Correlation matching transformation transformers for uhd image restoration,

    C. Wang, J. Pan, W. Wang, G. Fu, S. Liang, M. Wang, X.-M. Wu, and J. Liu, “Correlation matching transformation transformers for uhd image restoration,” inProceedings of the AAAI Conference on Artificial Intelligence, vol. 38, no. 6, 2024, pp. 5336–5344

    2024

  73. [73]

    Retinexmamba: Retinex- based mamba for low-light image enhancement,

    J. Bai, Y . Yin, Q. He, Y . Li, and X. Zhang, “Retinexmamba: Retinex- based mamba for low-light image enhancement,” inInternational Con- ference on Neural Information Processing. Springer, 2024, pp. 427– 442

    2024

  74. [74]

    Mamballie: Im- plicit retinex-aware low light enhancement with global-then-local state space,

    J. Weng, Z. Yan, Y . Tai, J. Qian, J. Yang, and J. Li, “Mamballie: Im- plicit retinex-aware low light enhancement with global-then-local state space,”Advances in Neural Information Processing Systems, vol. 37, pp. 27 440–27 462, 2024

    2024

  75. [75]

    Cwnet: Causal wavelet network for low-light image enhancement,

    T. Zhang, P. Liu, Y . Lu, M. Cai, Z. Zhang, Z. Zhang, and Q. Zhou, “Cwnet: Causal wavelet network for low-light image enhancement,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2025, pp. 8789–8799

    2025

  76. [76]

    Hvi- cidnet+: Beyond extreme darkness for low-light image enhancement,

    Q. Yan, K. Shi, Y . Feng, T. Hu, P. Wu, G. Pang, and Y . Zhang, “Hvi- cidnet+: Beyond extreme darkness for low-light image enhancement,” arXiv preprint arXiv:2507.06814, 2025

    work page arXiv 2025

  77. [77]

    Learning deep cnn denoiser prior for image restoration,

    K. Zhang, W. Zuo, S. Gu, and L. Zhang, “Learning deep cnn denoiser prior for image restoration,” inProceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 3929–3938

    2017

  78. [78]

    Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising,

    K. Zhang, W. Zuo, Y . Chen, D. Meng, and L. Zhang, “Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising,”IEEE transactions on image processing, vol. 26, no. 7, pp. 3142–3155, 2017

    2017

  79. [79]

    Plug- and-play image restoration with deep denoiser prior,

    K. Zhang, Y . Li, W. Zuo, L. Zhang, L. Van Gool, and R. Timofte, “Plug- and-play image restoration with deep denoiser prior,”IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 10, pp. 6360– 6376, 2021

    2021

  80. [80]

    Restormer: Efficient transformer for high-resolution image restoration,

    S. W. Zamir, A. Arora, S. Khan, M. Hayat, F. S. Khan, and M.-H. Yang, “Restormer: Efficient transformer for high-resolution image restoration,” inProceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022, pp. 5728–5739

    2022

Showing first 80 references.