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REVIEW 3 major objections 4 minor 27 references

Heterogeneous Mixture of Experts for Remote Sensing Image Super-Resolution

T0 review · 3 major / 4 minor · reviewed 2026-08-08 · deepseek-v4-flash

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Pith's one-line read This paper claims that a heterogeneous mixture-of-experts upsampling head with dual routing reconstructs remote sensing images more accurately than uniform upsampling heads, reporting higher PSNR and SSIM than prior state-of-the-art…

desk verdict Incremental but sensible MoE-based upsampling for remote sensing SR; the architecture is plausible, but the reported superiority over SOTA is not established because the evaluation rests on one split with test-set tuning and tiny margins. read the letter →

arxiv 2502.09654 v2 pith:XAHADY6Z submitted 2025-02-12 eess.IV cs.CV

Bowen Chen , Keyan Chen , Mohan Yang , Zhengxia Zou , Zhenwei Shi This is my paper · ORCID
classification eess.IVcs.CV
keywords remotesensingimagesuper-resolutionmixtureofexpertsheterogeneousdualroutingmulti-levelfeatureaggregationpixel-levelselectionUCMercedAID
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 the final upsampling stage of a remote sensing super-resolution network should not treat every pixel the same. It introduces MFG-HMoE, which replaces a single fixed upsampling layer with a mixture of expert upsamplers: small convolution-plus-pixel-shuffle heads with different kernel sizes, grouped so experts within a group share an architecture while groups differ. A multi-level feature aggregation module pools shallow and deep backbone features to drive two routing steps, first selecting an expert group for each pixel and then selecting the best expert inside that group. The paper reports that this raises PSNR and SSIM over a uniform upsampling head and over prior state-of-the-art models at 2x and 4x magnification on UCMerced and AID. The intended significance is that specialized per-pixel reconstruction patterns, rather than only a stronger uniform network, are what let SR models cope with mixed ground objects such as buildings, vegetation, and vehicles.

What carries the argument

The load-bearing object is the Dual-Routing Heterogeneous Mixture of Experts (DR-HMoE) upsampling head. Each expert is a convolution followed by pixel shuffle, $E_{ij}(\cdot)=\mathrm{PS}(\mathrm{Conv2D}(\cdot))$, indexed by group $i$ and in-group position $j$. The group router computes selection probabilities $p^k_i$ by softmax over the aggregated feature $x^{\mathrm{agg}}_k$ for $N$ groups; the expert router computes $p^k_j$ using the same aggregated features plus a position encoding of the active group; and the final pixel output is the probability-weighted combination $X^k_{\mathrm{out}}=\sum_{j\in G_i} p^k_i p^k_j E_{ij}(x^{\mathrm{feat}}_k)$ over the top-$K$ selected experts. The Multi-level Feature Aggregation (MFA) module supplies the router input by summing transformed outputs of all RHAG blocks, $X_{\mathrm{agg}}=\mathrm{LeakyReLU}(\mathrm{Conv2D}(\mathrm{LN}(\sum_i X_i')))$. The mechanism's job is to give each pixel a specialized upsampling path; the ablations credit the expert mixture, the MFA guidance, and the dual-routing step with successive gains.

What would settle it

Re-run the x4 UCMerced comparison with three or more different random splits of the 21 scene categories into training and testing, keeping the same 3:1 ratio and the same 100,000-iteration budget, and record the PSNR gap between MFG-HMoE and TTST; if the gap changes sign under any split, or if a test set built with bicubic downsampling instead of bilinear downsampling reverses the ranking, the paper's central superiority claim would be refuted.

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Extended reading notes

Core claim

The central claim is that heterogeneity plus dual routing in the upsampling head produces measurable super-resolution gains on remote sensing imagery. Concretely, with $N=2$ expert groups, $M=8$ experts per group, and $K=1$ selected expert per pixel, the best configuration pairs $1\times1$ and $3\times3$ convolutional upsamplers; on UCMerced $\times4$ it reaches 29.2882 dB PSNR and 0.7954 SSIM, above TTST's 29.1455 dB and 0.7896, and on AID $\times2$ it reaches 36.5399 dB PSNR and 0.9443 SSIM. The paper also claims the routing is semantically meaningful: relatively smooth regions such as vegetation, water, and shadows, and small objects such as vehicles, select one expert group, while larger objects with richer detail such as buildings and oil tanks select the other. This division of labor is presented as evidence that heterogeneous experts are required, not merely helpful, for remote sensing scenes.

Load-bearing premise

The evaluation protocol that supports the superiority claim uses a single random train/test split per dataset, low-resolution images generated only by bilinear downsampling, and a fixed 100,000-iteration training budget with the best checkpoint selected; if the reported ranking does not survive other random splits or other degradation models, the claim of general superiority over state-of-the-art methods collapses.

Editorial extensions

If this is right

  • On UCMerced and AID at both $\times2$ and $\times4$, MFG-HMoE reports higher PSNR and SSIM than each of the eight compared baselines: EDSR-L, RRDBNet, RCAN, SwinIR, HAT, HAUNet, SPT, and TTST.
  • The ablation study shows the heterogeneous mix matters: replacing 16 $1\times1$ experts with the optimal 8 $1\times1$ plus 8 $3\times3$ experts, and adding dual routing, raises $\times4$ UCMerced PSNR from 29.1938 dB to 29.2882 dB.
  • Adding the MFA module to a 16-expert homogeneous mixture raises $\times4$ UCMerced PSNR from 29.1938 dB to 29.2631 dB, showing that shallow-plus-deep routing cues contribute beyond expert count.
  • The routing visualizations indicate that expert groups acquire a semantic division of labor, with smooth and small objects sent to one group and large detailed objects to the other.
  • Because the RHAG backbone is retained and only the upsampling head is changed, the proposed head can be grafted onto existing feature extraction networks without architectural retraining of the backbone.

Reading between the lines

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

  • A natural test of the semantic-specialization claim would be to count, across many random seeds and splits, which expert group each UCMerced or AID scene class selects; stable class-to-group mappings would strengthen the claim that the routers encode ground-object categories rather than local texture.
  • Because low-resolution images are produced only by bilinear downsampling, the method's robustness to more realistic degradations such as motion blur plus sensor noise is untested; an extension would train the same routers on bicubic downsampling or on paired real-world low/high-resolution imagery and check whether the gains persist.
  • The paper fixes $K=1$ for the top-1 expert selection; a cheap extension is to test $K=2$ or $K=3$ with the soft weighting in Eq. (7) to see whether combining more experts per pixel yields additional accuracy.
  • If per-pixel routing is the source of the gain, the same dual-routing head should transfer to non-remote-sensing super-resolution benchmarks; comparing MFG-HMoE's head on natural-image SR datasets is a direct way to separate the benefit of heterogeneous routing from the benefit of the RHAG backbone.
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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 / 4 minor

Summary. The manuscript proposes MFG-HMoE, a super-resolution model for remote sensing images that replaces the uniform upsampling layer of an existing backbone (RHAG/HAT) with a heterogeneous mixture-of-experts head. The head organizes upsampling experts into N groups with different kernel sizes, routes each pixel first to an expert group and then to top-K experts using a multi-level feature aggregation signal (Eqs. (5)-(7)), and is trained end-to-end. Experiments are reported on UCMerced and AID at ×2 and ×4 scales, comparing with eight published SR methods; the proposed model shows the highest PSNR and SSIM in Tables I-II. Ablations on UCMerced ×4 (Table III) attribute the gains to the MoE head, the multi-level feature aggregation, and the dual-routing mechanism. The code is promised at a public GitHub URL.

Significance. The idea of routing pixels to heterogeneous upsampling experts is sensible and the paper provides a concrete instantiation with a public code link. If the reported gains are reproducible, the contribution is a useful modular upsampling head that can be attached to existing remote-sensing SR backbones. However, the empirical support is currently too thin to establish the advertised 'superior' claim: the reported margins over the strongest baseline are often smaller than typical training noise (e.g., AID ×4 PSNR +0.0059 dB vs. TTST), and the evaluation protocol uses one split, no repeated seeds, no validation-based model selection, and test-set hyperparameter choice. The architectural components are validated in the expected direction in the ablations, which is a strength, and the paper is clearly written.

major comments (3)
  1. [Section III-A and Tables I-II] The central 'superior' claim is not supported by the reported statistics because each model is trained once on a single random split with no repeated seeds or confidence intervals. The margins over the strongest baseline TTST are as small as +0.0059 dB PSNR and +0.0002 SSIM on AID ×4 (Table II), and +0.0258 dB on AID ×2; such differences may be within typical seed-to-seed variation in SR training, so the ranking is not demonstrably stable. Please provide repeated-seed experiments with mean and standard deviation (or error bars) for the proposed method and the strongest baselines, and adjust the abstract and conclusion claims accordingly.
  2. [Section III-A and Table III] Hyperparameters N=2, M=8, K=1 and the 1×1+3×3 expert combination are selected based on UCMerced ×4 test-set numbers in Table III, and the same UCMerced test set is then used for the final comparison in Table I. In addition, the text states that 'the best-performing model was selected for evaluation' without describing a validation split, so model selection appears to use test data as well. This makes the UCMerced results optimistically biased and does not provide an honest out-of-sample comparison. Please introduce a held-out validation set for model and hyperparameter selection, or explicitly report the selection procedure and present unbiased test-set results.
  3. [Section III-C and Table III] The ablation table is internally inconsistent in its current form: the first row has fewer entries than the number of columns, the row '16 0 16 0 0.7924 29.1938' appears twice, and several Group 4 and Group 5 rows do not align with the column headers. Because this table is the evidence for the MFA, dual-routing, expert-number, and kernel-size conclusions, it must be corrected and rechecked before the design claims can be assessed.
minor comments (4)
  1. [Section II-C, Eq. (5)] The notation W_i ∈ R^{C×N} appears inconsistent with a linear map from a C-dimensional pixel feature to N group scores; each group should have a weight vector W_i ∈ R^C, or the matrix should be W ∈ R^{N×C}. The same issue appears for W_i^j in Eq. (6).
  2. [Section III-A] The baseline list says 'SPT [16]', but the reference list and Tables I-II identify SPT as reference [15]; reference [16] is the saliency-aware dynamic routing method, so the citation should be corrected.
  3. [Section III-A] The metric protocol is incomplete: please specify whether PSNR and SSIM are computed on the Y channel and whether a border is cropped before evaluation, as is standard in the SR literature.
  4. [Section II-C, Eq. (7)] If K > 1, the gating weights are sums of p_i p_j over the selected experts and are not renormalized to 1; please clarify whether this is intentional and whether the implementation uses exactly this weighting.
Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The ledger is short because this is an empirical architecture paper, not a derivation. The free parameters are the hand-chosen architecture hyperparameters; the axioms are the domain assumptions needed for the benchmark results to support the stated claim. No new physical entities are introduced. The central caveat is that architecture choices were tuned on the same test set used for the headline results.

free parameters (4)
  • Number of expert groups N = 2
    Chosen in ablation (Table III) on the UCMerced test set; the dual-routing architecture depends on this split into heterogeneous groups.
  • Number of experts per group M = 8
    With N=2 this gives 16 total experts, the count selected by ablation in Table III; controls routing capacity.
  • Top-K experts selected per pixel K = 1
    Set in Section III-A; determines how many expert outputs are blended in Eq. 7 and hence the effective upsampling behavior.
  • Expert kernel size combination = 1x1 group and 3x3 group
    Selected via Table III as the best heterogeneous combination; the central motivation that different ground objects need different upsampling kernels rests on this choice.
assumptions (4)
  • domain assumption The RHAG backbone [22] is a suitable and sufficiently strong feature extractor, so the upsampling head is the bottleneck being improved.
    The method keeps RHAG fixed and only changes the upsampling stage; if a weaker backbone or a different feature distribution were used, the advantage of the heterogeneous experts could disappear.
  • domain assumption Pixels can be meaningfully assigned to experts based on aggregated multi-level features before upsampling.
    Equations 5-7 use the aggregated feature map to route each pixel; the paper assumes the routing signal is predictive of which upsampling kernel will reconstruct that pixel best.
  • domain assumption Bilinear downsampling and the reported train/test splits are representative of remote sensing SR evaluation.
    Section III-A creates LR images by bilinear interpolation and uses a single random split; no multi-seed or multi-degradation validation is reported.
  • standard math Standard softmax normalization and weighted summation are valid for combining expert outputs.
    Equations 5-7 rely only on standard probability normalization and linear combination, which are established.

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Pith. "Pith review of Heterogeneous Mixture of Experts for Remote Sensing Image Super-Resolution." pith.science (2026). https://pith.science/paper/XAHADY6Z

@misc{pith2026250209654,
  author       = {Pith},
  title        = {Pith review of: Heterogeneous Mixture of Experts for Remote Sensing Image Super-Resolution},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XAHADY6Z}},
  note         = {Machine review of arXiv:2502.09654}
}
read the original abstract

Remote sensing image super-resolution (SR) aims to reconstruct high-resolution remote sensing images from low-resolution inputs, thereby addressing limitations imposed by sensors and imaging conditions. However, the inherent characteristics of remote sensing images, including diverse ground object types and complex details, pose significant challenges to achieving high-quality reconstruction. Existing methods typically employ a uniform structure to process various types of ground objects without distinction, making it difficult to adapt to the complex characteristics of remote sensing images. To address this issue, we introduce a Mixture of Experts (MoE) model and design a set of heterogeneous experts. These experts are organized into multiple expert groups, where experts within each group are homogeneous while being heterogeneous across groups. This design ensures that specialized activation parameters can be employed to handle the diverse and intricate details of ground objects effectively. To better accommodate the heterogeneous experts, we propose a multi-level feature aggregation strategy to guide the routing process. Additionally, we develop a dual-routing mechanism to adaptively select the optimal expert for each pixel. Experiments conducted on the UCMerced and AID datasets demonstrate that our proposed method achieves superior SR reconstruction accuracy compared to state-of-the-art methods. The code will be available at https://github.com/Mr-Bamboo/MFG-HMoE.

Figures

Figures reproduced from arXiv: 2502.09654 by the authors.

Figure 1
Figure 1. The flowchart of the proposed MFG-HMoE. The feature extraction network is constructed by stacking RHAGs [22]. The MFA module aggregates [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 3
Figure 3. Visualization of the selected experts in [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 2
Figure 2. Comparison of results from different methods in ×4 SR on the UCMerced dataset with the HR ground truth. C. Ablation Studies Table III presents the ablation study results on the UCMerced dataset. The first row shows the baseline model with a single upsampling layer. The experimental results demonstrate several key findings: 1) Group 1 reveals the impact of MoE that incorporating 16 experts significantly improves the … view at source ↗

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Reference graph

Works this paper leans on

27 extracted references · 23 canonical work pages

  1. [16]

    Lightweight stepless super- resolution of remote sensing images via saliency-aware dynamic routing strategy,

    H. Wu, N. Ni, and L. Zhang, “Lightweight stepless super- resolution of remote sensing images via saliency-aware dynamic routing strategy,” IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1–17, 2023

    work page 2023

  2. [15]

    Scale-aware backprojection transformer for single remote sens- ing image super-resolution,

    J. Hao, W. Li, Y . Lu, Y . Jin, Y . Zhao, S. Wang, and B. Wang, “Scale-aware backprojection transformer for single remote sens- ing image super-resolution,” IEEE Transactions on Geoscience and Remote Sensing , 2024

    work page 2024

  3. [1]

    Resolution- agnostic remote sensing scene classification with implicit neural representations,

    K. Chen, W. Li, J. Chen, Z. Zou, and Z. Shi, “Resolution- agnostic remote sensing scene classification with implicit neural representations,” IEEE Geoscience and Remote Sensing Letters , vol. 20, pp. 1–5, 2022

    work page 2022

  4. [2]

    Target detection in hyper- spectral remote sensing image: Current status and challenges,

    B. Chen, L. Liu, Z. Zou, and Z. Shi, “Target detection in hyper- spectral remote sensing image: Current status and challenges,” Remote Sensing , vol. 15, no. 13, p. 3223, 2023

    work page 2023

  5. [3]

    Remote sensing image change captioning with dual-branch transformers: A new method and a large scale dataset,

    C. Liu, R. Zhao, H. Chen, Z. Zou, and Z. Shi, “Remote sensing image change captioning with dual-branch transformers: A new method and a large scale dataset,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–20, 2022

    work page 2022

  6. [4]

    Rscama: Remote sensing image change captioning with state space model,

    C. Liu, K. Chen, B. Chen, H. Zhang, Z. Zou, and Z. Shi, “Rscama: Remote sensing image change captioning with state space model,” IEEE Geoscience and Remote Sensing Letters , 2024

    work page 2024

  7. [5]

    Mambads: Near-surface meteorological field downscaling with topography constrained selective state space modeling,

    Z. Liu, H. Chen, L. Bai, W. Li, W. Ouyang, Z. Zou, and Z. Shi, “Mambads: Near-surface meteorological field downscaling with topography constrained selective state space modeling,” IEEE Transactions on Geoscience and Remote Sensing , 2024

    work page 2024

  8. [6]

    Rsprompter: Learning to prompt for remote sensing instance segmentation based on visual foundation model,

    K. Chen, C. Liu, H. Chen, H. Zhang, W. Li, Z. Zou, and Z. Shi, “Rsprompter: Learning to prompt for remote sensing instance segmentation based on visual foundation model,” IEEE Transactions on Geoscience and Remote Sensing , vol. 62, pp. 1–17, 2024

    work page 2024

Show all 27 references
  1. [7]

    Spectral- cascaded diffusion model for remote sensing image spectral super-resolution,

    B. Chen, L. Liu, C. Liu, Z. Zou, and Z. Shi, “Spectral- cascaded diffusion model for remote sensing image spectral super-resolution,” IEEE Transactions on Geoscience and Re- mote Sensing , 2024

    2024

  2. [8]

    Super-resolution for remote sensing images via local–global combined network,

    S. Lei, Z. Shi, and Z. Zou, “Super-resolution for remote sensing images via local–global combined network,” IEEE Geoscience and Remote Sensing Letters , vol. 14, no. 8, pp. 1243–1247, 2017

    2017

  3. [9]

    Diverse hyper- spectral remote sensing image synthesis with diffusion models,

    L. Liu, B. Chen, H. Chen, Z. Zou, and Z. Shi, “Diverse hyper- spectral remote sensing image synthesis with diffusion models,” IEEE Transactions on Geoscience and Remote Sensing , vol. 61, pp. 1–16, 2023

    2023

  4. [10]

    Deep learning for single image super-resolution: A brief re- view,

    W. Yang, X. Zhang, Y . Tian, W. Wang, J.-H. Xue, and Q. Liao, “Deep learning for single image super-resolution: A brief re- view,” IEEE Transactions on Multimedia , vol. 21, no. 12, pp. 3106–3121, 2019

    2019

  5. [11]

    Hybrid-scale self-similarity exploitation for remote sensing image super-resolution,

    S. Lei and Z. Shi, “Hybrid-scale self-similarity exploitation for remote sensing image super-resolution,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–10, 2021

    2021

  6. [12]

    Enhanced window-based self-attention with global and multi-scale representations for remote sensing image super- resolution,

    Y . Lu, S. Wang, B. Wang, X. Zhang, X. Wang, and Y . Zhao, “Enhanced window-based self-attention with global and multi-scale representations for remote sensing image super- resolution,” Remote Sensing , vol. 16, no. 15, p. 2837, 2024

    2024

  7. [13]

    Ttst: A top-k token selective transformer for remote sensing image super-resolution,

    Y . Xiao, Q. Yuan, K. Jiang, J. He, C.-W. Lin, and L. Zhang, “Ttst: A top-k token selective transformer for remote sensing image super-resolution,” IEEE Transactions on Image Process- ing, 2024

    2024

  8. [14]

    Dy- namicvis: An efficient and general visual foundation model for remote sensing image understanding,

    K. Chen, C. Liu, B. Chen, W. Li, Z. Zou, and Z. Shi, “Dy- namicvis: An efficient and general visual foundation model for remote sensing image understanding,” arXiv preprint arXiv:2503.16426, 2025

    2025

  9. [17]

    Adaptive mixtures of local experts,

    R. A. Jacobs, M. I. Jordan, S. J. Nowlan, and G. E. Hin- ton, “Adaptive mixtures of local experts,” Neural computation , vol. 3, no. 1, pp. 79–87, 1991

    1991

  10. [18]

    Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity,

    W. Fedus, B. Zoph, and N. Shazeer, “Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity,” Journal of Machine Learning Research , vol. 23, no. 120, pp. 1–39, 2022

    2022

  11. [19]

    Bag-of-visual-words and spatial extensions for land-use classification,

    Y . Yang and S. Newsam, “Bag-of-visual-words and spatial extensions for land-use classification,” in Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems , 2010, pp. 270–279

    2010

  12. [20]

    Aid: A benchmark data set for performance evaluation of aerial scene classification,

    G.-S. Xia, J. Hu, F. Hu, B. Shi, X. Bai, Y . Zhong, L. Zhang, and X. Lu, “Aid: A benchmark data set for performance evaluation of aerial scene classification,” IEEE Transactions on Geoscience and Remote Sensing , vol. 55, no. 7, pp. 3965–3981, 2017

    2017

  13. [21]

    Rsmamba: Remote sensing image classification with state space model,

    K. Chen, B. Chen, C. Liu, W. Li, Z. Zou, and Z. Shi, “Rsmamba: Remote sensing image classification with state space model,” IEEE Geoscience and Remote Sensing Letters , 2024

    2024

  14. [22]

    Acti- vating more pixels in image super-resolution transformer,

    X. Chen, X. Wang, J. Zhou, Y . Qiao, and C. Dong, “Acti- vating more pixels in image super-resolution transformer,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2023, pp. 22 367–22 377

    2023

  15. [23]

    Enhanced deep residual networks for single image super-resolution,

    B. Lim, S. Son, H. Kim, S. Nah, and K. Mu Lee, “Enhanced deep residual networks for single image super-resolution,” in Proceedings of the IEEE conference on computer vision and pattern recognition workshops , 2017, pp. 136–144

    2017

  16. [24]

    Esrgan: Enhanced super-resolution generative adversarial networks,

    X. Wang, K. Yu, S. Wu, J. Gu, Y . Liu, C. Dong, Y . Qiao, and C. Change Loy, “Esrgan: Enhanced super-resolution generative adversarial networks,” in Proceedings of the European confer- ence on computer vision (ECCV) workshops , 2018, pp. 0–0

    2018

  17. [25]

    Image super-resolution using very deep residual channel attention net- works,

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

    2018

  18. [26]

    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,” in Proceed- ings of the IEEE/CVF international conference on computer vision, 2021, pp. 1833–1844

    2021

  19. [27]

    Hybrid attention-based u-shaped network for remote sensing image super-resolution,

    J. Wang, B. Wang, X. Wang, Y . Zhao, and T. Long, “Hybrid attention-based u-shaped network for remote sensing image super-resolution,” IEEE Transactions on Geoscience and Re- mote Sensing , vol. 61, pp. 1–15, 2023

    2023

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Reviewed August 8, 2026 · model on record in the stance chip above.