Title: Spiking Meets Attention: Efficient Remote Sensing Image Super-Resolution with Attention Spiking Neural Networks
Venue: NeurIPS 2025
Accepted: yes
Keywords: Remote sensing imagery

super-resolution

spiking neural network
Abstract: Spiking neural networks (SNNs) are emerging as a promising alternative to traditional artificial neural networks (ANNs), offering biological plausibility and energy efficiency. Despite these merits, SNNs are frequently hampered by limited capacity and insufficient representation power, yet remain underexplored in remote sensing image (RSI) super-resolution (SR) tasks. In this paper, we first observe that spiking signals exhibit drastic intensity variations across diverse textures, highlighting an active learning state of the neurons. This observation motivates us to apply SNNs for efficient SR of RSIs. Inspired by the success of attention mechanisms in representing salient information, we devise the spiking attention block (SAB), a concise yet effective component that optimizes membrane potentials through inferred attention weights, which, in turn, regulates spiking activity for superior feature representation. Our key contributions include: 1) we bridge the independent modulation between temporal and channel dimensions, facilitating joint feature correlation learning, and 2) we access the global self-similar patterns in large-scale remote sensing imagery to infer spatial attention weights, incorporating effective priors for realistic and faithful reconstruction. Building upon SAB, we proposed SpikeSR, which achieves state-of-the-art performance across various remote sensing benchmarks such as AID, DOTA, and DIOR, while maintaining high computational efficiency. Code of SpikeSR will be available at https://github.com/XY-boy/SpikeSR.

Spiking Meets Attention: Efficient Remote Sensing Image Super-Resolution with Attention Spiking Neural Networks

Abstract

Spiking neural networks (SNNs) are emerging as a promising alternative to traditional artificial neural networks (ANNs), offering biological plausibility and energy efficiency. Despite these merits, SNNs are frequently hampered by limited capacity and insufficient representation power, yet remain underexplored in remote sensing image (RSI) super-resolution (SR) tasks. In this paper, we first observe that spiking signals exhibit drastic intensity variations across diverse textures, highlighting an active learning state of the neurons. This observation motivates us to apply SNNs for efficient SR of RSIs. Inspired by the success of attention mechanisms in representing salient information, we devise the spiking attention block (SAB), a concise yet effective component that optimizes membrane potentials through inferred attention weights, which, in turn, regulates spiking activity for superior feature representation. Our key contributions include: 1) we bridge the independent modulation between temporal and channel dimensions, facilitating joint feature correlation learning, and 2) we access the global self-similar patterns in large-scale remote sensing scenarios to infer spatial attention weights, incorporating effective priors for realistic and faithful reconstruction. Building upon SAB, we proposed SpikeSR, which achieves state-of-the-art performance across various remote sensing benchmarks such as AID, DOTA, and DIOR, while maintaining high computational efficiency. Code of SpikeSR will be available at https://github.com/XY-boy/SpikeSR .

High-resolution remote sensing images (RSIs) contain fine-grained object structures and textures, which are critical for accurate interpretation in downstream tasks [42, 11, 6]. However, limited by the intrinsic resolution of airborne sensors, RSI can merely capture partial spatial details, resulting in suboptimal scene representation and visual quality. Image super-resolution (SR) aims to alleviate this problem by reconstructing high-resolution (HR) images from low-resolution (LR) observations [53, 60]. Despite this, SR remains a challenging ill-posed issue, as a degraded input may correspond to multiple plausible outputs.

Early efforts rely on hand-crafted priors to tame the ill-posedness, e.g. , nonlocal mean [67, 7] and gradient profile [48], but they are often trapped in limited performance and scalability. Recent advances in artificial neural networks (ANNs), e.g. , CNNs and Transformers, have witnessed remarkable progress in SR with large-capacity models [49, 50, 3, 4, 71]. However, they often come with a

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trade-off of increased computational overhead and growing storage costs, making them less efficient in practical scenarios, particularly when reconstructing large-scale RSIs.

More recently, brain-inspired spiking neural networks (SNNs), as the third generation of neural networks, have emerged as a promising alternative for energy-efficient intelligence [25, 59, 40]. Different from ANNs that encode features as continuous values, SNNs can emulate biological communication with discrete spiking signals and propagate them by neurons, thus enjoying lower power consumption. As depicted in Fig. 1(a), our experiments reveal a novel finding that spiking neurons maintain an active learning state across LR RSIs, even in severely damaged textures. Specifically, we observed that degraded RSIs exhibit smoothed pixel intensities and obscured sharp details, posing a significant challenge to characterize high-frequency representations. In contrast, spiking signals retain drastic responses and pronounced spike rates, highlighting that neurons remain in an active learning state. This naturally arises a question: Can SNNs leverage their inherent properties to handle image degradation for efficient yet high-quality RSI SR?

In fact, to effectively grasp complex and diverse spatial details in RSIs, the network must possess adequate capacity and representation power. Unfortunately, there are two critical challenges when adapting SNNs for SR tasks. Firstly, spiking activity in SNNs inevitably causes pixel-wise information loss, which hampers the representation capacity of SNNs, especially when the network deepens. This stems from the discrete nature of binary spiking signals, leading to undesirable spiking degradation problems [61, 15]. Secondly, SNNs remain constrained by suboptimal membrane potential dynamics, restricting effective exploration of global context during spiking communications. This necessitates a customized strategy to optimize membrane potentials, but is barely explored before.

To address these limitations, we propose SpikeSR, an SNN-based framework inspired by human visual attention mechanisms, which can actively represent image degradation and modulate synaptic weights to focus on salient regions, which, in turn, regulate the spiking activity for improved capacity and representation power. Specifically, SpikeSR employs a concise yet effective spiking attention block (SAB) to optimize feature emphasis through spiking response dynamics, which integrates three key innovations: 1) the combination of CNN and SNN layers to mitigate information loss induced by discrete spiking activity; 2) introducing hybrid dimension attention (HDA) to recalibrate spiking response across both temporal and channel dimensions, facilitating a joint feature correlations learning; 3) accessing global self-similarity patterns in RSIs to infer spatial attention weights, incorporating effective priors for realistic and faithful reconstruction. Compared to state-of-the-art (SOTA) ANN-based efficient SR models, our SpikeSR demonstrates lower model complexity and superior performance, as shown in Fig. 1(b).

Our contributions are summarized as follows:

We pioneer an attention spiking neural network for efficient SR of RSIs, providing a new perspective on developing efficient models in large-scale Earth observation scenarios.

We devise a concise yet effective SAB, which mitigates the information loss and regulates membrane potentials of spiking activity for improved representation of SNNs.

Extensive experimental results on various remote sensing datasets demonstrate that our SpikeSR achieves competitive SR performance against SOTA ANN-based methods.

1 Related Work

Deep Networks for SR . Inspired by the pioneering SRCNN [10], CNN-based SR methods have achieved remarkable progress, dominating the field for years. They mainly elaborated on the network design to tame the ill-posedness, with notable advances in residual connections [21, 32] and attention mechanisms [69, 39]. However, these methods often suffer from high computational complexity, e.g. , exhaustive non-local modeling [28, 38], making them less efficient in large-scale RSIs.

Recently, transformer-based SR models have demonstrated impressive performance, benefiting from their ability to model long-range dependencies. IPT [2] first introduces Transformers in SR field, but requires massive parameters and laborious pre-training processes. SwinIR [31] effectively reduces the model size by partitioning the image into smaller windows when applying multi-head attention mechanisms, while maintaining favorable performance. Against transformer, Mamba-based SR methods achieve comparable global model capacity with linear complexity [18, 57, 17]. Despite these advancements, advanced SR models are often trapped by rising computational overhead and growing storage costs, posing significant concerns in real-world applications, particularly in remote sensing scenarios.

Efficient SR Models. To reduce computational budget, CARN [1] utilizes grouped convolutions and a cascading mechanism to improve the residual architecture. IMDN [20] progressively distills useful information during feature extraction and applies network pruning to further decrease complexity. FMEN [12] optimizes residual modules to accelerate inference. In Transformer-based SR methods, SPIN [66] enhances long-range modeling by combining self-attention with pixel clustering, facilitating interactions between superpixels. HiT-SR [68] expands the self-attention receptive field by applying different window sizes of hierarchal layers. Despite these successes, there is still room to further boost SR performance. Moreover, the potential of energy-efficient SNNs for SR tasks remains largely unexplored.

Spiking Neural Networks. Recent advances in neuromorphic computing have shown the great potential of SNNs in computational efficiency and power as CNNs [63, 64]. Currently, SNNs have been successfully applied to various tasks, such as image classification [25, 44], object detection and tracking [22, 59], optical flow estimation [26, 40], etc .

A common solution to build SNNs is converting pre-trained ANN models [8, 62]. Li et al. [30] proposed a layer-wise calibration to minimize activation mismatch during conversion. Ding et al. [9] replaced ReLU with the rate norm layer, enabling direct conversion from a trained ANN to an SNN. Stockl et al. [46] used time-varying multi-bit spikes to better approximate activation functions. However, conversion-based methods face accuracy gaps and high latency due to extensive time-step simulations, resulting in increased latency and energy consumption.

An alternative involves using agent gradient functions for continuous relaxation of non-smooth spike activities, enabling direct training via backpropagation through time. Lee et al. [27] treated membrane potential as a signal to overcome discontinuities, enabling direct training from spikes. Wang et al. [54] introduced an iterative LIF model and proposed spatiotemporal backpropagation based on approximate peak activity derivatives. Later, Zheng et al. [70] proposed temporal delay batch normalization, which significantly enhanced the depth of SNNs. To bridge the performance gap between ANNs and SNNs, some methods borrowed insights from CNNs, applying residual learning [15, 19] and attention mechanisms [65, 41] to SNNs. Nonetheless, there has been limited exploration of pixel-level regression tasks, such as SR.

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The architecture of the proposed SpikeSR is illustrated in Fig. 2, which mainly consists of SAGs. Before SAGs, we utilize a 3 × 3 convolution to extract high-dimensional features from the LR input. These features are then processed through m stacked SAGs to explore deeper representations. Each SAG includes n SABs, a SCB, and a residual connection. In the SCB, leaky integrate-and-fire (LIF) neurons [37, 14] are used to convert the inputs into binary spike sequences ( i.e. , 0 or 1). As shown in Fig. 2, the output of the LIF neuron is 1 when the membrane potential exceeds the threshold, and 0 otherwise. To optimize the membrane potential, we introduce HDA, which refines the spiking activity using an efficient temporal-channel joint attention [72]. Furthermore, the proposed DSA is employed to introduce global context for accurate SR. After the terminal SAG, an FB is utilized to convert the spike sequence features into continuous values. Finally, SpikeSR generates super-resolved output from the fused features by applying pixel-shuffle [43] and a 3 × 3 convolutional layer.

2.1 Spiking Attention Block

As evidenced in Fig. 1, regions degraded by different factors exhibit noticeable fluctuations when encoded by LIF, highlighting pronounced firing spike rates of neurons. This provides robust and latent informative spiking cues from LR images. Unlike ANNs that encode images into continuous decimal values, SNNs use discrete binary spike values for neuronal communication, and thus demonstrate undesirable information loss [24, 65], resulting in limited capacity to represent degraded LR images. To address this, the design philosophy of the SAB is focused on leveraging CNNs and attention mechanisms to regulate membrane potentials, facilitating high-quality feature representation for SR, which in turn affects the spiking activity.

As shown in Fig. 2, in particular, the output of the n -th SAB at the t -th time step is denoted as X t,n , and can be obtained by the following:

where ¯ X t,n 1 and ¯ X t,n 2 are two feature representations obtained from parallel branches, defined by:

where Conv represents a 3 × 3 convolution layer, and tdBN means the threshold-dependent batch normalization.

Different from previous works that focus solely on separate temporal and channel modulation [61, 65, 45], SAB adheres to temporal-channel joint attention [72] to realize joint adjustment of the spike response in HDA, effectively achieving interdependencies between the temporal and channel scopes. More details of HDA can be found in the Appendix.

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Non-local self-similarity has been recognized as an effective prior for SR tasks [47]. However, existing non-local attention mechanisms are computationally expensive due to exhaustive non-local operations, which impedes their efficiency in large-scale RSIs. In contrast, the proposed DSA efficiently grasps complex self-similar patterns in RSIs at the patch level to infer intricate spatial weights. Then, we utilize the cross-attention (CA) paradigm to enhance long-range communication, facilitating the fusion of useful context.

The details of DSA are shown in Fig. 3. Considering that the object scale exhibits explicit diversity in RSIs, the input feature F is downsampled using bilinear interpolation, forming a multi-scale feature pyramid. For clarity, we demonstrate this process by dividing the initial features into 9 patches. Following the design in [33], the final DSA exploits a cascaded patch division strategy. Specifically, each patch is first average-pooled to capture its spatial characteristics, then reshaped and subjected to self-similarity computation, yielding a similarity matrix. The final self-similarity scores are fused via matrix multiplication to enhance the multi-scale representation. The best-matching patch ¯ P i with P i can be obtained by:

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where ¯ P i is the patch in ¯ F , and E means the operation of average pooling and feature reshaping. We adopt the Gumbel-Softmax [52] to achieve the non-differentiable argmax function.

Although matched patches contain highly relevant similarity, they are inevitably subject to mismatches and geometric transformations. Hence, we use deformable convolution (DConv) to reduce the generation of hallucinated textures. The deformable feature F D at location p 0 is computed as follows:

where ω ( p m ) is the convolution weight at relative location p m , ∆ p m is a 2D vector that represents the learnable offsets, R is a regular grid that determines the receptive field of the convolution kernel. For a 3 × 3 kernel, R = { ( -1 , -1) , ( -1 , 0) , · · · , (1 , 1) } . To fuse the self-similar features F D with ¯ F , we embed F D to Q , and ¯ F to K , V using fully connected layers, then perform aggregation by:

Finally, the fused features are summarized with the original features F and fed into the multilayer perceptron (MLP) to obtain the final output:

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To transform discrete spiking sequences into continuous pixel values, a common approach is to apply mean sampling along the time dimension. However, this naive process may lead to the loss of crucial spatial details, potentially affecting the SR quality. Therefore, we introduce a fusion block that adaptively aggregates spiking sequences and mitigates information loss. Given an input spike input Y , the computation process of FB can be formulated as:

where TA and SA denote temporal and spatial attention [65], σ means a sigmoid function and ⊗ denotes feature multiplication. The final output of FB is obtained by summing Y 1 and Y 2 .

3 Experiments

Datasets. We use the AID dataset [56] as the training set, a large-scale remote sensing benchmark for scene classification, consisting of 30 different scene categories. The AID dataset includes 10,000 HR images, where we randomly select 3,000 for training and 900 for validation. The LR samples are generated by bicubic downsampling. Following TTST [58], we also evaluate our method on the DOTA [55] and DIOR [29] datasets, which contain 900 and 1,000 images, respectively.

Implementation Details. During model training, the learning rate is fixed to 10 -4 , and the training procedure stops after 1000 epochs with a batch size of 4. Adam optimizer is used with β 1 = 0 . 9

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and β 2 = 0 . 999 . Data augmentation includes random rotations of 90°, 180°, 270°, and horizontal flips on 64 × 64 patches. The channel number, embedding dimension of cross-attention, and MLP rate of small, medium, and final SpikeSR are set to { 40 , 24 , 72 } , { 56 , 24 , 72 } , and { 64 , 32 , 100 } , respectively. The number of SAGs is 4, with 2 SABs in each SAG. Time step is set to T = 4 .

Metrics. The widely used peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) are used to evaluate SR performance. The results are measured on the Y channel after converting RGB to YCbCr space. For a fair comparison, all SR methods are retained on an RTX 4090 GPU from scratch using the AID dataset, adhering to their official implementation settings.

3.1 Comparison with Efficient Models

We compare our SpikeSR with state-of-the-art (SOTA) efficient SR methods, including CNN-based models of CARN [1], LatticeNet [36], RLFN [23], etc, and transformer-based approaches of SwinIR

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[31], SPIN [66], HiT-SR [68], etc . We also report results for the small and medium versions of our SpikeSR, denoted as SpikeSR-S and Spike-M, respectively.

Quantitative comparisons. The quantitative results of various methods are reported in Table 1. We can observe that our SpikeSR achieves the best performance across three benchmarks, outperforming SOTA CNN- and transformer-based SR models. For example, on AID, DOTA, and DIOR datasets, SpikeSR improves PSNR by 0.08 dB, 0.04 dB, and 0.1 dB, respectively, compared to the impressive SwinIR. Moreover, the small version of SpikeSR requires fewer parameters (472K vs. 897K) and FLOPs (15.21G vs. 23.56G) than SwinIR, yet achieves superior average performance.

Qualitative comparisons. Fig. 4, Fig. 5, and Fig. 6 present visual comparisons on AID, DOTA, and DIOR datasets, respectively. As shown in Fig. 4, SpikeSR effectively restores severely damaged textures, e.g. , the runway line in the playground. By contrast, other SR models fail to recover such weak high-frequency details. In Fig. 5, the reconstruction of 'img_591' highlights that SpikeSR produces results closest to the GT, while other methods like SPIN recovers unrealistic results. Moreover, Fig. 6 further demonstrates that SpikeSR consistently delivers superior visual quality, restoring more textural information compared to large-capacity ANN-based model like Omni-SR.

4 Ablation Study

We conduct ablation studies to assess the impact of key components in SpikeSR. The experimental results in Table 3 are measured on the AID-tiny dataset [58]. In particular, the Baseline model is constructed by removing the HDA and DSA and replacing them with standard temporal attention (TA), channel attention (CA), and spatial attention (SA) mechanisms. For a fair comparison, we increase the number of m , n , and channel dimensions to 8, 4, and 256, respectively, which ensures a similar number of parameters with our SpikeSR. Similarly, those settings of Variant-A are modified to 10, 5, and 128, respectively.

Effectiveness of HDA and DSA. Table 3 indicates how the SR performance is influenced by the HDA and DSA. Comparing the PSNR values of the Baseline and Variant-A reveals that HDA contributes a 0.12 dB improvement. This suggests that enhancing the correlation between the temporal and channel dimensions delivers better recalibration of the membrane potentials. More intuitively, we visualize the feature maps to highlight the impact of HDA, as shown in Fig. 7(b). The results illustrate that HDA effectively refines the feature representation by emphasizing salient details and suppressing irrelevant information.

By introducing the proposed DSA to the Baseline model, the PSNR results can be improved by a large margin of 0.21 dB. When employing HDA and DSA simultaneously, the resulting SpikeSR achieves an additional 0.08 dB improvement compared to Variant-B. To better demonstrate its effectiveness in capturing global self-similarity priors, we provide the LAM visualization and diffusion index in Fig. 7(a). As observed, DSA generates more pronounced LAMs and significantly increases the DI, indicating that the model activates more valuable pixels for accurate SR.

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Feature Pyramid and DConv. Due to the scale diversity of objects in RSIs, we constructed a feature pyramid to grasp the self-similarity in multiple levels. As listed in Table 4, the use of feature pyramid improves the performance by 0.05 dB without introducing additional parameters. Furthermore, to demonstrate the effectiveness of deformable convolution, we remove this component, which leads to a severe performance drop by 0.12 dB. This illustrates that the self-similar patches contain massive irrelevant and misaligned contents, and direct fusion may introduce interference, thus resulting in suboptimal performance.

Network Depth. We evaluate the impact of the network depth by changing the number of SAGs of our SpikeSR from 2 to 6 blocks. As reported in Table 5, SpikeSR achieves the highest SR performance when m = 3 . While increasing the number of m may further improve the reconstruction, it also brings larger model size. Therefore, we set m = 4 in our final model, considering the trade-off between performance and computational complexity.

5 Conclusion and Limitation

In this paper, we investigate the application of SNNs for efficient SR of remote sensing images. Motivated by the observation that LIF neurons exhibit a higher spike rate in degraded images, we integrate SNNs with convolutions for improved feature representation. Besides, a hybrid dimension attention is employed to modulate the spike response, further refining salient information. To incorporate valuable prior knowledge for more accurate SR, we propose a deformable similarity attention module, capturing global self-similarity across multiple feature levels. Extensive experiments on various remote sensing datasets demonstrate the efficacy and effectiveness of the proposed SpikeSR model. We believe our exploration can facilitate the practical application of energy-efficient models in remote sensing area.

SpikeSR still has some limitations. First, its representational capacity remains improvable. Second, the reliance on attention mechanisms introduces additional computational overhead, limiting efficiency. We leave these issues for future exploration.

Acknowledgment. This work is supported in part by the National Natural Science Foundation of China (423B2104, 623B2080), and in part by the Natural Science Foundation of Heilongjiang Province of China for Excellent Youth Project (YQ2024F006), and in part by the Open Research Fund from Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) (GML-KF-24-09).

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work presents an innovative framework (SpikeSR) for remote sensing image super-resolution, where spiking neural networks are combined with attention mechanisms, providing a well-motivated SNN-based paradigm for improved efficiency and SR quality. To start with, the authors reveal the SNN's inherent properties for handling image degradation and highlight two challenges in adapting SNN to SR -- pixel-wise information loss and suboptimal membrane potential dynamics. To mitigate these issues, a Spiking Attention Block (SAB) is proposed by dynamically regulating membrane potentials through inferred attention weights, thus enhancing feature representation. Based on SAB, the proposed SpikeSR demonstrates promising performance compared against SOTA methods, as validated by comprehensive ablation and experiments over multiple datasets.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The authors make the first attempt of SNN-based RSI SR, and correctly highlight that the limited representation capacity of SNNs has hindered their application in SR tasks, which makes it a valuable and highly original contribution to the field.\n2. The observation of active learning states in spiking signals and their connection to the attention modulation adds valuable understanding to SNN capabilities, providing a new perspective on developing efficient models in large-scale Earth observation scenarios.\n3. Achieving performance comparable to or even surpassing that of ANNs using SNNs is non-trivial. SpikeSR outperforms advanced ANN-based approaches and has been thoroughly validated on multiple large-scale remote sensing benchmarks, demonstrating a significant technical superiority. \n4. The proposed SpikeSR is concise yet effective. The manuscript is well-organized and clearly written, making it easy to follow.\n\nWeaknesses:\n1. It is recommended to clarify whether the deformable convolution is applied at the patch level or the full-image level.\n2. The number of time steps T is a crucial parameter for SNNs. Please consider providing more ablation on different selection of T (e.g., 1, 2, 4, 8) for analyzing its impact.\n3. Does the pixel-level intensity smoothing observed in Fig. 1a imply a reduced ability of the ANN-based model to capture fine-grained details. This is intuitively important for understanding the motivation of the proposed SpikeSR and should be further discussed or explicitly pointed out.\n4. In the ablation section, the details for the AID-tiny used by the authors are unclear. It is recommended to release this dataset publicly to ensure experimental reproducibility.\n\nQUESTIONS:\nPlease check the Weaknesses above on the usage of deformable convolution, ablation on the time step T, more clarification on Fig.1a and details on AID-tiny.\n\nIt is also recommended to correct some typos (though few in the work), for example, typos following the values 90, 180, and 270. \nPossibly a title for Sec.1 introduction is missed.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces SpikeSR, a novel framework for remote sensing image super-resolution based on Spiking Neural Networks (SNNs). SpikeSR leverages the energy efficiency and sparsity of Leaky Integrate-and-Fire (LIF) neurons to reduce computational overhead, while incorporating attention-based mechanisms to enhance feature representation in degraded low-resolution images. The proposed method includes several key components: a Hybrid Dimension Attention (HDA) module, which recalibrates spiking responses across temporal and channel dimensions to facilitate joint feature correlation learning; a Deformable Similarity Attention (DSA) module, which captures multi-scale spatial self-similarity via deformable attention to align and integrate relevant textures; and a Fusion Block that adaptively merges spike sequences guided by both temporal and spatial attention to mitigate information loss. Experiments on the AID, DOTA, and DIOR datasets demonstrate that SpikeSR achieves superior performance compared to existing CNN- and Transformer-based SR methods, while maintaining high computational efficiency.\n\nSTRENGTHS AND WEAKNESSES:\n1. Quality\n While the paper presents a technically sound framework and demonstrates solid empirical performance, its contribution would be further strengthened by a deeper exploration of the unique characteristics of Spiking Neural Networks (SNNs). The current architecture—based on a standard LIF-based spiking backbone with Transformer-inspired attention modules—largely follows a modular design pattern, and it remains somewhat unclear to what extent it fully capitalizes on the temporal and sparse properties of SNNs. The proposed HDA and DSA modules are effective, yet their connection to the underlying spiking dynamics appears limited, and similar designs could potentially be adapted to non-spiking models as well. Additionally, although the motivation highlights the temporal modeling capabilities of SNNs, the paper provides limited analysis regarding how specific temporal factors—such as spike timing, membrane integration behavior, or variations in time steps—impact reconstruction performance. A more thorough investigation along these lines could clarify the specific advantages offered by spike-based representations in the SR context.\n2. Clarity\nThe manuscript is generally clear and well-organized: the motivation is well-articulated and the problem formulation is clear. The architecture diagrams (especially Fig. 3) are informative and visually aid understanding. However, the description of the key component, the Hybrid Dimension Attention (HDA) module, is relatively brief in the main text. Although the authors mention that more details are provided in the appendix, including a more comprehensive explanation of its structure and working mechanism in the main body would significantly enhance readability and overall clarity.\n3. Originality\nWhile the application of Spiking Neural Networks (SNNs) to image super-resolution is relatively novel, the architectural innovation of this work appears to be incremental. The proposed framework follows a modular “SNN backbone + attention mechanism” design, with its core components—such as deformable attention and dimension-wise recalibration—drawing from established techniques in CNN and Transformer literature. As a result, the contribution seems to focus more on the integration and adaptation of known methods .Further investigation into how spiking dynamics interact with SR-specific challenges—such as texture restoration or temporal information flow—could enhance the originality and domain relevance of the work.\n4. Significance\nWhile the method demonstrates solid empirical performance with low computational overhead, its broader impact is somewhat constrained by the limited exploration of the underlying mechanisms that make spiking neural networks effective for super-resolution. That said, the direction itself—adapting spike-based computation to dense prediction tasks such as SR—is timely and meaningful. In a field where energy efficiency and model compactness are increasingly important, the attempt to leverage the temporal dynamics and sparsity of SNNs is conceptually promising. Therefore, although the current implementation may lack theoretical depth, the research direction it represents is worth exploring, as it has the potential to develop into a more generalizable and insightful solution and drive further progress in the field of efficient vision models.\n\nQUESTIONS:\nClarification on the HDA module: The Hybrid Dimension Attention (HDA) is a core component of your model, yet its description in the main text is quite limited. While you mention that further details are provided in the appendix, the current version of the submission does not appear to include any appendix. For reproducibility and a clearer understanding of the model’s contribution, please provide a detailed explanation of the HDA module in the main paper or ensure the appendix is available and sufficiently descriptive. Clarifying this could significantly strengthen the clarity and transparency of the proposed method.\n\nThe neuron voltage visualization in Figure 1(a) is an interesting perspective. However, the connection between spiking responses and actual SR reconstruction quality remains somewhat speculative. Could the authors clarify whether regions with high voltage activity (as shown)  correspond to better preserved or recovered high-frequency content in the final SR outputs?\n\nA key motivation for using SNNs lies in their ability to capture temporal dynamics. However, the paper lacks any systematic analysis of how these dynamics—such as the number of simulation steps or LIF parameters—affect performance. Ablation studies or controlled experiments along this axis would significantly strengthen the argument that the model benefits from intrinsic spiking properties.\n\nWhile the architecture is thoughtfully designed and the integration of components is well-executed, it largely builds upon existing ideas—spiking neurons combined with attention mechanisms such as temporal-channel recalibration and deformable similarity. The paper would benefit from a more detailed explanation of what unique representational benefits or inductive biases this combination offers. At present, the novelty appears to lie more in the architectural composition than in a fundamentally new conceptual insight.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents SpikeSR, a novel framework based on Spiking Neural Networks (SNNs) for remote sensing image super-resolution. The authors propose a Spiking Attention Block (SAB) that effectively optimizes membrane potentials through inferred attention weights, thereby regulating spiking activity for better feature representation. The key contributions of this work include bridging the modulation between temporal and channel dimensions and leveraging global self-similarity patterns in large-scale remote sensing scenarios to infer spatial attention weights. The proposed method achieves state-of-the-art performance across various remote sensing benchmarks while maintaining high computational efficiency.\n\nSTRENGTHS AND WEAKNESSES:\nPros:\n\n1. Remote sensing image super-resolution has practical applications.\n\n2. The paper was well written and organized.\n\n3. Validation was performed on various datasets and ablation experiments were performed.\n\nCons:\n\n1. Lack of clear motivation. From the current manuscript, it is not clear the advantages of applying SNN to remote sensing images of SR. What are its benefits over lightweight SR networks?\n\n2. Does not show significant novelty. It appears that this work simply combines SNN with an attention mechanism where some simple improvements are made to the attention mechanism.\n\n3. Performance is not good enough. This work suffers from a performance disadvantage even when compared to HiT-SR for natural images. In fact, there are many cutting-edge SR methods that are not included in the comparison.\n\n4. The biggest advantage of SNN is low energy consumption. There is a complete lack of energy consumption related comparisons in the table.\n\n5. Regarding inference efficiency, memory overhead related comparisons are missing.\n\nQUESTIONS:\nCould the authors provide a more detailed theoretical analysis of the proposed SAB, particularly regarding how the inferred attention weights influence the optimization of membrane potentials and the subsequent improvement in feature representation?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe core content of this paper is to propose an efficient remote sensing image super-resolution (SR) method based on spiking neural networks (SNNs), named SpikeSR. This method optimizes the performance of SNNs in processing remote sensing image super-resolution tasks by combining the attention mechanism while maintaining high computational efficiency.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths\n1. For the first time, the spiking neural network (SNN) is combined with the attention mechanism for remote sensing image super-resolution (SR), and the spike attention block (SAB) and SpikeSR framework are proposed, filling the gap of SNN in the field of remote sensing SR.\nHDA jointly optimizes spatiotemporal and channel features to solve the information loss problem caused by discrete pulses in SNN.\nDSA uses the global self-similarity prior of remote sensing images to alleviate misaligned matching through deformable convolution and improve the authenticity of reconstruction.\n\n2. The experimental design is sufficient, the multi-scenario verification covers 30 types of remote sensing scenes, the ablation experiment is sufficient, and the contribution of HDA/DSA is analyzed.\n\n### Weaknesses\n1. The paper emphasizes the \"high energy efficiency\" of SNN, but does not provide power consumption comparison data (such as comparison with ANN's Joule/inference), and only uses FLOPs to indirectly reflect efficiency. Can the author provide information such as inference speed and resource usage?\n2. HDA relies on the external module TCJA, but does not explain how to adapt to the pulse characteristics of SNN (such as membrane potential dynamics), and the technical details are not complete.\n\nQUESTIONS:\nSame weakness as above, see it.\n\nLIMITATIONS:\nSame weakness as above, see for details.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-07-03"}