Spiking Meets Attention: Efficient Remote Sensing Image Super-Resolution with Attention Spiking Neural Networks
Authors: Yi Xiao
Qiangqiang Yuan
Kui Jiang
Wenke Huang
Qiang Zhang
Tingting Zheng
Chia-Wen Lin
Liangpei Zhang
Venue: NeurIPS 2025 poster ()
Keywords: Remote sensing imagery
super-resolution
spiking neural network
Primary area: applications

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
Yi Xiao
Qiangqiang Yuan"*
Kui Jiang" Chia-Wen Lin
Wenke Huang² Qiang Zhang' Liangpei Zhang
Tingting Zheng
School of Computer and Artificial Intelligence, Zhengzhou University Wuhan University HHarbi Institution of Technology "Dalian Maritime University
National Tsinghua University
yixiaoezzu. edw.zz.ueeuucco
Abstract
Spiking neural networks (SNNs) are emerging as promising alternative to tra- ditional 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 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 poten- tials 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, facili- tating joint feature correlation learning, and 2) we access the global self-similar patterns in large-scale remote sensing scenarios infer spatial attention weights, incorporating effective priors for realistic and faithful reconstruction. Building upon SAB, we proposed SpikeSR, which achieves state-of-the-an performance across various remote sensing benchmarks such as AID, DOTA, and DIOR, while maintaining high computational efficiency. Code of SpikeSR will be available peatps://githan com XY boy/bbo//o Spny/SpikeRRR
High-resolution remote sensing images (RSIs) contain fine- ane-grained object structures and textures, which critical for accurate interpretation in downstream tasks [22 (1.6 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 alleviate this problem by reconstructing high-resolution (HR) images from low-resolution (L.R) observations 533 60]. Despite this, SR remains challenging ill ill-posed issue, degraded input may correspond to multiple plausible outputs.
Early efforts rely on hand-crafted priors tame the ill-posedness, e,g. nonlocal mean 67 7 and gra- dient 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
7T. However, they often come with
*Corresponding Author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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(a) Pixel Intensilt (lop) and Nouron Voltage (down) Maps
(b) Performance and Efficiency Comparison
Figure 1: (a) The visualization of pixel intensity and neuron voltage in images under various degradation factors reveals important insights. The pixel intensity map illustrates that the high-frequency components of the image tend be smooth, indicating reduction in sharp details during progressive downsampling. Neuron intensity maps, derived from a LIF model [37 [44, show that high- freh-frquency details persist with drastic fluctuations, suggesting that the neurons remain in an active state. (b) FLOPs and PSNR performance comparison. The circle sizes represent the number of parameters. Our SpikeSR outperforms SOTA efficient SR methods with high efficiency. PSNR results averaged the AID, DOTA, and DIOR datasets.
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 promising alternative for energy-efficien intelligence [25] 59 40).
Different from ANNs that encode features as continuous values, SNNs can emulate biological com- munication with discrete spiking signals and propagate them by neurons, thus enjoying lower power consumption. As depicted Fig. aa), our experiments reveal a novel finding that spiking neurons maintain active learning state across LR RSIs, in severely damaged textures. Specifically, observed that degraded RSIs exhibit smoothed pixel intensities and obscured sharp details, posing 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 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 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 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 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 sfte-offtthe-an (SOTA) ANN- efficient SR models, our SpikeSR demonstrates lower model complexity and superior performance, as shown Fig. bb).

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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 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.
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 21132) and attention
mechanisms (69 [39 However, these methods often suffer from high computational complexity, egg.
exhaustive non-local modeling 28 38) making them less efficient in large-scale RSIs
Recently, transformer- bbssdd 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 [3 effectively reduces the model size by partitioning the image into smaller windows when applying multi-head attention mechanisms, while maintaining favorable performance. Against transformer, hamba-based SR methods achieve comparable global model capacity with linear complexity [88] (57 [1]. 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 [ utilizes grouped convolutions and cascading mechanism 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- bbase SR methods, SPIN
66 enhances long-range modeling by combining self-attention with pixel clustering, facili- tating interactions between superpixels. HiT-SR (68) expands the self-attention receptive field by applying different window sizes of hierarchal layers. Despite these successes, there 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 object detection and
tracking 22 59 optical flow estimation 26 40) etc.
A common solution to build SNNs is converting pre-trained ANN models g 62 Li et al. [30] proposed a layer-wise wise calibration to minimize activation mismatch during conversion. Ding al. 99] replaced ReLU with the rate norm layer, enabling direct conversion from trained ANN to an SNN. Stockl al. (46) used time-varying multi-bit spikes better approximate activation functions. However, conversion-basel 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 er 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 [5] 19 and attention mechanisms 655 41 to SNNs. Nonetheless, there has been limited exploration of livel-level regression tasks, such SR.
3

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Input
Sequence
Output
Spiking A ltention Group (SAG)
Povalbbutie
Catv
SAG
SA
SAB
Cone
Temporal Duplication
Spiking Allention Block (SAB)
Leaky Integrate and Fire Neuron (LIF)
Fusiion Block
Rybrd Dimeraion
Deforobie Sinitatty
Xiod
Roae Nearco
a10i0ni
M
(HDA)
(DSA)
W.
Fre
Spaed
1:3
0100101.
Spiking Convolution (SCB)
LIB
Cotr
LII
Cotr
MDN
x
Vi
0100001
29 Hanptnaken Spulal Attention
Figure 2: Overall network architecture of SpikeSR. The LR input is replicated along the temporal dimension and then processed through a convolution to extract shallow features. The core module SpikeSR SAG, which employs SABs to capture deep spiking representations. Each SAB contains three main components: (1) SCB, (2) HDA, and (3) DSA. The fusion block (FB) aggregates the spatial-temporal sequences, and pixclshuffle is used to reconstruct the SR output.
Method
The architecture of the proposed SpikeSR is illustrated in Fig. 2 which mainly consists of SAGs. Before SAGs, we utilize a 3 x 3 convolution extract dighdiimessional features from the LR input. These features are then processed through stacked SAGs to explore deeper representations. Each SAG includes n SABs, SCB, and residual connection. the SCB, leaky integrate-and-fire (LIFF 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 L.IF neuron when the membrane potential exceeds the threshold, and 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, 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-shufile 43 and 3 3 convolutional layer.
2.1 Spiking Attention Block
As evidenced in Fig. 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 24 655, resulting 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--hh SAB at the 1-th time step denoted X and can be obtained by the following:
X = x + DSA x..)))
where X and lin are two feature representations obtained from parallel branches, defined by:
X f. SCB(SCB(XX" FD LdBN(Conv(Xtn- (X
2
where Conv represents a 3 x 3 convolution layer, and tdBN means the threshold-depeendnt batch normalization.
Different from previous works that focus solely on separate temporal and channel modulation 61
SAB adheres to temporal-channel joint attention 772 to realize joint adjustment of the spike response HDA, effectively achieving interdependencies between the temporal and channel scopes. More details of HDA can be found in the Appendix.
65 45

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Delormable Convolation Block
Dedemndda Paapiag Frelacs
Reternco Proton @ Addiis
3+3+C
BxC
Metie Prodect @ Mtltgliaaton
Averago Pooling
Restape
Salf- Siniliriy
CA
MLP
3-3
Se3wCC
Averae Pooling
Reshape
Suzillate
Indioss
9xc
P
B
P
1-2
3=3+C
9-C
Average Pooling
Reshage
Selt- Sirillatiy
F,
Figure 3: The illustration our DSA. Note that set the diagonal elements similarity matrix zero before selecting the indices the highest scores. The deformable convolution operates at the patch level, alleviating the mismatch between the most similar patches.
2.2 Deformable Similarity Attention
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 RSIs at the patch level to infer intricate spatial weights. Then, utilize the cross-attention (CA) paradigm enhance long-range communication, facilitating the fusion of useful context.
Non-local self-similarity has been recognized as effective prior for SR tasks 47.
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, Following the design [3] the final DSA exploits cascaded patch division strategy. Specifically, each patch is first average-poode poooled to capture its spatial characteristics, then reshaped and subjected to self-similarity computation, yielding a similarity matrix. The final self self-similarity scores are fused via matrix multiplication to enhance the multi-scale representation. The best- best-matching patch P, with P; can be obtained by:
demonstrate this process by dividing the initial features into patches.
P: argmax E(P,) J P,
(3)
where P, 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 con-differentiable argmax function.
Although matched patches contain highly relevant similarity, they are inevitably subject to mismatches and geometric transformations. Hence, use deformable convolution (DConv) to reduce the generation of hallucinated textures. The deformable feature FD at location Po is computed as follows:
F FP(po)
Σ w(pm) F(po + Pm + Apm)
Pm ER
where w(pm) is the convolution weight relative location Pm. Apm 2D vector that represents the learnalle offsets, R is a regular grid that determines the receptive field of the convolution kernel. For 3 3 kernel, R {(-1, 1), (-),0),
(1, 1), To fuse the self-similar features FD with F, we embed FD to Q, and F to K. V using fully connected layers, then perform aggregation by:
= softmax Softmax(QK QCT////T/V /Vdjv,
(5)
Finally, the fused features are summarized with the original features F and fed into the multilayer perceptron (MLP) to obtain the final output:
= MLP(F + V).
(6)
5

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Table 1: Quantitative comparison of SpikeSR with SOTA methods three remote sensing datasets. FL.OPs are
measured corresponding LR image of 160 160 pixels. Note that T evaluate the model
complexity SpikeSR for comparison.
AID
56
DOTA
[55
DIOR
29
Average
Methods
#Param.
FLOPs
PSNR
SSIM
PSNR
SSIM
PSNR
SSIM
PSNR
SSIM
Bicubic
28.86
0.7382
31.16
0.7947
28.57
0.7432
29.53
0.7587
SRCNN
10
20K
0.512G
29.70
0.7741
32.10
0.8264
29.49
0.7768
30.43
0.7924
VDSR 2
667K
17.08G
30.44
0.8004
33.22
0,8569
30.36
0.8036
31.34
0.8203
EDSR 32
1518K
50.77G
30.65
0.8086
33.64
0.8648
30.63
0.8116
31.64
0.8283
CARN
1112K
40.39G
30.66
0.8068
33.66
0.8633
30.64
0.8102
31.65
0.8268
IMDN 20
715K
18.18G
30.71
0.8076
33.70
0.8641
30.73
0.8115
31.71
0.8277
RFDN-L. 64
681K
16.49G
30.69
0.8074
33.73
0.8642
30.72
0.8114
31.71
0.8277
Lat
36
777K
19.39G
30.73
0.8089
33.75
0.8653
30.75
0.8126
31.74
0.8289
HNCT 13
364K
8.48G
30.79
0.8104
33.83
0.8664
30.80
0.8136
31.81
0,8301
FMEN 12
1046K
26.72G
30.65
0.8063
33.66
0.8631
30.66
0.8104
31.66
0.8266
RI.FN 23
544K
13.25G
30.70
0.8074
33.69
0.8636
30.70
0.81
31.70
0.8273
ESKT 35
752K
26.06G
30.77
0.8102
33.75
0.8668
30.81
0.8142
31.78
0.8304
SwinlR Right [31
897K
23.56G
30.83
0.8114
33.94
0.8677
30.85
0.8149
31.87
0.8313
Omni-SR [5
2803K
70.98G
30.89
0,8142
33.94
0.8695
30.89
0.8170
31.91
0.8336
NGswin
995K
12.73G
30.79
0.8107
33.87
0.8667
30.79
0.8140
31.82
0.8305
SPIN 666
555K
18.91G
30.78
0.8098
33.85
0.8673
30.82
0.8139
31.82
0,8303
HiT-SR
792K
21.04G
30.87
0.8138
33.93
0.8167
31.90
0.8331
SpikeSK-S S (Ours)
472K
15.21G
30.86
0.8126
33.89
0.8687
30.89
0.8162
31.88
0.8325
SpikeSR-M (Ours)
763K
24.00G
30.88
0.8133
33.92
0.8689
30.90
0.8163
31.90
0.8328
SpikeSR (Ours)
1042K
33.05G
30.91
0.8142
33.98
0,8700
30.95
0.8175
31.95
0.8339
Bicubic
EDSR
CARN
IMDN
LatticeNet
playground_3 270
ESRT
SwinIR-ligh
SPIN
SpikeSR
Ground Truth
Figure 4: Qualitative comparison of SOTA efficient models for x4 SR task AID test set.
2.3
Fusion Block
To transform discrete spiking sequences into continuous pixel values, 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 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
Y = (TA(Y & Y, Y, = (SA(Y)) @ (1-Y)).
where TA and SA denote temporal and spatial attention 665, means a sigmoid function and x denotes feature multiplication. The final output is obtained by summing Y, and Yz.
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 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 (2) datasets, which contain 900 and 1, 1,000 images, respectively.
Implementation Details. During model training, the learning rate fixed to 10-4, and the training procedure stops after 1000 epochs with a batch size of 4. Adam optimizer is used with B1 = 0.9

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Table 2: Quantitative comparison of SpikeSR with SOTA methods on 30 scene types of AID datasets.
EDSR
3
CARN I
IMDN 20
ESRT
33
SwinlR-L.
[3]
SPIN
66
HiT-SR
68
SpikeSR
Soene types
PSNR
SSIM
PSNR
SSIM
PSNR
SSIM
PSNR
SSIM
PSNR
SSIM
PSNR
SSIM
PSNR
SSIM
PSNR
SSIM
Airport
29.93
0.8282
29.96
0.8264
30.00
0.8270
30.09
0.8292
30.14
0.8307
30.11
0.8245
30.21
0.8525
30.27
0.8336
Bare
36.94
0.8837
36.92
0.8829
0.8834
0.8840
0.8841
36.96
0.8843
37.00
0.8846
36.98
0.8846
Baseball Field
33.05
0.8765
33.06
0.8753
33.17
0.8763
33.21
0,8773
33.27
0.8782
33.14
0,8759
33.29
0.8791
33.32
0.8794
34.18
0.8727
34.27
0.8762
0.8764
Bridge
32.93
0.8800
32.86
0.8774
32.93
0.8784
33.05
0.8803
33.15
0.8810
33.05
0.8803
33.14
0.8820
33.27
0.8827
Center
28.77
0.7921
28.71
0.7881
28.79
0.7892
28.88
0.7423
29.00
0.7954
88
0.7919
29.03
0.7974
29.09
0.7985
Church
0.7469
0.7449
0.7467
0.7489
0.7512
0.7507
0.7549
26.70
0.7560
Commercial
29.01
0.75400
29.11
0.7544
29.17
0.7958
29.22
0.7975
29.28
0.7996
29.27
0.7989
29.33
0.8021
29.37
0.8033
D-Rosidential
24.38
0.6839
0.6898
0,6924
0,6872
0.6998
0.6978
Desert
40.20
0.9268
40.22
0.9259
40.17
0.9264
40.06
0.9276
40.31
0.9275
40.24
0.9279
40.27
0,9282
40.25
0.9283
Farmlind
35.00
0.8683
34,89
0.8656
34,94
0.8667
34.99
0.8679
35.02
0,8681
34.98
0,8678
35.09
0.8696
35.09
0.8700
29.99
29.90
0.7350
29.97
0.7350
0.7395
30,05
Industrial
28.88
0.7931
28.84
0.7894
28.88
0.7904
28.98
0.7944
29.05
0.7959
28, 98
0.7932
29.11
0.7998
29.16
0.8007
Meadow
34.63
0.7804
34.53
0.7769
0.7784
34,66
0.7807
0,7807
34,68
0.7820
M4-Residntial
28.34
0.7365
28.39
0.7347
28.42
0.7349
28.47
0.7370
28.56
0.7401
28.39
0.7334
28.64
0.7443
28.63 0.7436
Mountain
30.63
0.7885
30.70
0.7892
30.70
0.7895
30.74
0.7909
30,78
0.7921
30.75
0.7911
30.79
0.79330
30.79 0.79.30
0.8177
30.73
0.8162
30,82
0.8203
Parking
27.25
0.8317
27.08
0.8245
27.23
0.8270
27.47
0.8352
27.42
0.8354
27.50
0.8363
27.70
0,8435
27.72
0.8424
Playground
35.37
0.8943
35.27
0.892
35.42
0,8929
35.47
0.8942
0,8946
0.8946
35.59
0.8968
35.70
0.8976
Pond
32.11
0.8542
32 0.8532
32
0.8534
32.17
0.8546
32.22
0.8553
32.15
0.8545
32.23
0.8561
32.25
0.8563
Port
28.50
0.8596
28.61
0.8593
28. 67
0.8597
28.75 75
0.8620
28
0,8611
28.79
0,8624
28 85
0,8651
28.94
0.8658
Railway Station
0.7738
28.92
0.7777
28.89
0.7759
28.97
0.7802
29.02
Resort
28.52
0.7799
28.59
0.7791
28.62
0.7795
28.68
0.7819
28.
0.7837
28, 66
0.7801
78
0.7864
28.82 0,7869
River
31.55
0.7891
31,55
0.7882
31,57
0.7885
31,60
0.7900
0,7905
31.61
0.7904
66
0.7918
31,68 0.7922
School
29.36
0.8044
29.41
0.8053
29.45
0.8041
29.51
0.8067
29.59
0.8091
29.50
0,8048
29.67
0.8123
29.68 0.8124
S-Residential
27.71
0.6728
27.79
0.6723
27,80
0.6285
27.85
0.6752
27 88
0.6758
80 0.6728
27.91
0.6782
27.92 0.6775
Square
30,97
30.98
0.821
0.8256
31.15 0.8266
Stadium
29.63
0.8387
29.51
0.834
29.62
0.8358
74
0.8388
29 82 0.8413
29.76
0.8394
29 80
0.8420
29.93 0.8439
Stonge Tanks
27.44
0,7664
27.50
0.7649
27.52
0.7648
27.58
0.7671
27.63
0,7692
27.58
0.767
56
0.7720
27.68 0,7718
Vinduct
28.99
0.7757
28.92
0.7711
28.96
0,7722
29.06
0,7759
29.14
0,7784
29.05
0.7753
29.16
0.7811
29.25
0.7831
Average
30.65
0.8086
30.66
0.8068
30.71
0.8076
30.77
0,8102
30.83
0.8114
30.78
0.8098
30.87
0,8138
30,91
0.8142
Bicubic
EDSR
Dicubie
DSKE
DOTA: ing 591
SwiEL
SPIN
Ours
G2
DOTA: ung 596
SWIHTR
SPIN
Onrs
GT
Bicuttic
EDSR
CARN
ESRT
Dieulic
EDSR
CAXN
USKT
DOTA: inn_ 675
Swin.R
SPIN
Ours
07
DOTA: ing 856
SAInIR
SPIN
Ours
GT
Figure 5: Qualitative comparison of SOTA efficient SR models for x4 SR task on DOTA dataset.
and B3 = 0.999. Data augmentation includes random rotations of 90' 180", 270%, and horizontal flips 64 x 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, 100). respectively. The number of SAGs is 4, with 2 SABs in each SAG. Time step set to T 4.
Metrics. The widely used peak signal-to-noie ratio (PSNR) and structural similarity (SSIM) are used to evaluate SR performance. The results measured 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-basee models of CARN [1. LatticeNet [36), RLFN (23), ctc, and transformer- approaches of SwinIR
7

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Bicubic
EDSR
CARN
LatticeNet
IMDN
FMEN
SwinIR
Omni-SS SR
SPIN
SpileSR
Figure 6: Qualitative comparison of SOTA efficient SR models for x4 SR task on DIOR dataset.
31, SPIN [66], HiT-SR (68), etc. We also report results for the small and medium versions of our Sp, denoted as Sp and Spike-M, respectively.
Quantitative comparisons. The quantitative results of various methods are reported in Table I We can observe that our SpikeSR achieves the best performance across three benchmarks, outperforming 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 FLOPSs (15.21G vs. 23.56G) than SwinIR, yet achieves superior average performance.
SOTA CNN- and transformer-ban
Qualitative comparisons. Fig. 4 Fig. and Fig. 6 present visual comparisons on AID, DOTA, and DIOR datasets, respectively. As shown in Fig. 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. []] the reconstruction of "img _mm__591"
591" highlights that SpikeSR produces results closest to the GT, while other methods like SPIN recovers unrealistic results. Moreover, Fig. [ further demonstrates that SpikeSR consistently delivers superior visual quality, restoring more textural information compared large-capacity ANN-based model like Omni-SR.
Ablation Study
conduct ablation studies to assess the impact of key components 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 fair comparison, 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. TabieBJindicates how the SR performance is influenced by the HDA and DSA. Comparing the PSNR values of 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, shown in Fig. 7 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 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. a]. 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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CARN
HIT-SR
w/o DSA
w/ DSA (Ours)
w/o HDA
(a) Attrilattoon Maps
(1) Fasture Visualization
w/ HIDA
Reference Image
DI: 2.51
DI: 9.82
DI: 6.87
DC: 13.79
Figure 7: (a) Analysis of local attribution maps (LAMs) T6 and diftusion index (DI). The proposed DAS helps SpikeSR exploit more useful information against CARN and HiT-SR. (b) Feature visualizations. The feature obtained by HDA is sharper and preserves more details, indicating hugl-quality feature representations.
Table 3: Ablation on different variants of our SpikeSR.
Methods
TA
CA
SA
HDA
DSA
#Param.
PSNR (dB)
Baseline
1120K
27.80
Variant-A
1062K
27.92
Variant-B B
1009K
28.11
SpikeSR
1042K
28.19
Table 4: Ablation on feature pyramid and de- formable convolution.
Table 5: Performance and complexity analysis of SpikeSR with different numbers of blocks.
Methods
w/o Pyramid w/o DConv DSA (Full)
Blocks
2
3
4
6
#Param,
1042K
918K
1042K
Param.
558K
800K
1042K
1284K
1526K
FLOPs
31.41G
28.86G
33.00G
FLOPS
17.47G
25.26G
33.00G
40.84G
48.60G
PSNR (dB)
28.14
28.07
28.19
PSNR (dB)
28.11
28.17
28.19
28.16
28.20
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 the use of feature pyramid improves the performance by 0.05 dB without introducing additional parameters. Furthermore, to demonstrate the effectiveness of deformable convolution, a severe performance drop by 0. 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.
remove this component, which leads to
Network Depth. We evaluate the impact of the network depth by changing the number of SAGs of our SpikeSR from 6 blocks. As reported in Table[5] SpikeSR achieves the highest SR performance when 3. While increasing the number of 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.
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, integrate SNNs with convolutions for improved feature representation. Besides, hybrid dimension attention employed to modulate the spike response, further refining salient information. To incor- porate valuable prior knowledge for more accurate SR, we propose 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-efficien models in remote sensing area.

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SpikeSR still has some limitations. First, its representational capacity remains improvable. Sec- ond, 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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Reviews:
[{"review_id": "8iRgfpcspF", "reviewer": "Reviewer_bD3C", "summary": "The 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.", "questions": "Same weakness as above, see it.", "limitations": "Same weakness as above, see for details.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 4, "originality": 3, "strengths_and_weaknesses": "### 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.", "was_revised": true, "final_justification": "Thanks to the author of the rebuttal. Judging from the supplementary efficiency comparison, while the proposed SpikeSR uses less energy, it does consume more GPU memory and inference time. Therefore, it is still an attempt to apply Spiking Neural Networks to remote sensing image super-resolution. I still think it is very innovative, but further research is needed.\n\nTherefore, I maintain my original rating.", "rebuttal": "## **Response to Reviewer bD3C**\n\nDear Reviewer bD3C:\n\nWe sincerely appreciate your time and effort in reviewing our paper. Thank you for your positive feedback on the significance of our work, especially recognizing our **first attempt** to combine spiking neural network (SNN) with attention mechanism for remote sensing image super-resolution. We are glad to hear that you find our SpikeSR **fills the gap of SNN in the field** of remote sensing SR, and that our **experimental design and ablation are sufficient**. We hope our responses below will address your concerns. We would greatly appreciate it if you would consider a score update.\n\n### Weaknesses\n**W1: Can the author provide more information to reflect efficiency?**\n\n A1: Thank you for this valuable comment. We have additionally evaluated three metrics to address this concern: (1) energy consumption (Joule/inference), (2) GPU memory usage, and (3) inference time. The results are presented in the table below. It can be found that our SpikeSR exhibits significantly lower energy consumption compared to ANN-based methods, which further demonstrates the **high energy efficiency of SNN**.  We will add a comprehensive discussion of model efficiency in our revised manuscript!\n\n| Methods | EDSR | CARN | SwinIR-light | OmniSR | NGswin | SPIN | HiT-SR | SpikeSR-S | SpikeSR-M | SpikeSR |\n| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n| Energy (pJ)  | 162.46 | 129.25 | 70.39 | 227.14 | 40.74 | 60.51 | 67.33 | **2.96** | 4.15 | 4.74 |\n| Memory (MB)  | 202.00 | 6.00 | 126.00 | 146.00 | 10.00 | **4.00** | **4.00** | 6.00 | 10.00 | 14.00 |\n| Times (s)  | 0.41 | **0.29** | 7.38 | 4.81 | 3.99 | 7.93 | 2.20 | 4.52 | 4.84 | 5.18 |\n\nIt should be noted that the energy consumption is a theoretical approximation, calculated based on the following principle: the energy cost of ANNs is derived from multiply-and-accumulate (MAC) operations, while that of SNNs comes from AC operations (i.e., an addition). In a typical CMOS system [1], one MAC operation consumes approximately 3.2 pJ, while one accumulate AC consumes about 0.2 pJ. The energy consumption of an ANN model can be estimated by $E_{\\text{ANN}} =  N_{\\text{MAC}} \\times E_{\\text{MAC}}$, where $N_{\\text{MAC}}$ is the number of MAC and $E_{\\text{MAC}}$ is set to 3.2. For an SNN model, the energy consumption depends on the number of AC operations, the synaptic activity sparsity, and the number of time steps. The estimated energy is computed as $E_{\\text{SNN}} =  s \\times T \\times N_{\\text{AC}} \\times E_{\\text{AC}}$, where $s$ denote the mean sparsity, which is set to 16.42% [2], $T$ is the number of time setp, $N_{\\text{AC}}$ means the additon mumber, and $E_{\\text{AC}}$ is set to 0.2.\n\nThe GPU consumption and inference time were evaluated on a single NVIDIA RTX 4090 GPU. The reported inference time is the total time required to super-resolve 100 LR images of size 160×160.\n\n**Reference**\n\n[1] Sze V, Chen Y H, Yang T J, et al. Efficient processing of deep neural networks: A tutorial and survey. Proceedings of the IEEE, 2017, 105(12): 2295-2329.\n\n[2] Guo Y, Chen Y, Liu X, et al. Ternary spike: Learning ternary spikes for spiking neural networks. Proceedings of the AAAI conference on artificial intelligence. 2024, 38(11): 12244-12252.\n\n**W2: Explain the adaptation and technical details of the proposed HDA.**\n\nThank you for your insightful comment. To effectively adapt the HDA module to the spiking dynamics of SNNs, we first apply a squeeze operation to compress membrane potentials into an average matrix. We then perform 1D convolutions independently along the temporal and channel dimensions to extract attention weights that reflect the dynamic importance of features across both scopes. To further model their interdependence, we employ element-wise multiplication to fuse two weights into a unified temporal-channel attention map, which is ultimately used to modulate the membrane potential representations. Instead of operating directly on raw spike trains, HDA enhances feature representation by dynamically adjusting membrane potentials, enabling it to leverage rich spatiotemporal information while respecting the discrete nature of SNNs. This explanation will be included in the revised manuscript!\n\nDue to space limitations, we have provided more technical details of HDA in the supplementary materials, which is ensured to be publicly accessible. Besides, we will release the source code to ensure technical completeness and reproducibility."}, {"review_id": "nOKvB43T0h", "reviewer": "Reviewer_DFR5", "summary": "This 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.", "questions": "Could 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?", "limitations": "Yes", "rating": 3, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Pros:\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.", "was_revised": true, "final_justification": "I appreciate the authors' detailed response. However, I must note that it did not adequately address my concerns. Specifically, the current manuscript appears to simply apply SNNs to remote sensing super-resolution without providing meaningful insights specific to this task. In my view, this work does not yet meet the threshold for NeurIPS. I am willing to raise the score by one point to a borderline rejection, but my overall assessment remains negative.", "rebuttal": "## **Response to Reviewer DFR5**\n\nDear Reviewer DFR5:\n\nThank you for your insightful comments. We are pleased that you think our SpikeSR is **a novel framework**, recognize its **practical applications**, and consider our manuscript **well-written** and **well-organized**. We appreciate your recognition of its achievement of **state-of-the-art performance** across multiple remote sensing benchmarks, while maintaining **high computational efficiency**. We hope our responses below address your concerns, and we kindly ask you to consider updating the score.\n\n### Weakness\n\n**W1: What are the benefits over lightweight SR networks?**\n\nA1: Thank you for your valuable feedback. Compared to lightweight SR networks, SNNs offer unique advantages due to their biological plausibility and sparse spatiotemporal dynamics. Thus:\n\n1) These characteristics enable SNNs to achieve **significantly lower energy consumption** (as listed in the table below), which motivates us to apply SNN to reconstruct large-scale and high-resolution remote sensing images.\n\n2) We observe spiking neurons maintain an active learning state across low-resolution images, even in severely damaged textures, which motivates us to leverage the inherent properties of SNNs to handle image degradation for efﬁcient yet high-quality SR.\n\n3) With the growing interest and demand for in-orbit processing of remote sensing data,  our method enjoys highly energy-efficient benefits and favorable performance, making it a promising solution for practical scenarios.\n\n| Methods | EDSR | CARN | SwinIR-light | OmniSR | NGswin | SPIN | HiT-SR | SpikeSR |\n| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n| Energy (pJ)  | 162.46 | 129.25 | 70.39 | 227.14 | 40.74 | 60.51 | 67.33 | **4.74** |\n| PSNR (dB)  | 31.64 | 31.65 | 31.87 | 31.91 | 31.82 | 31.82 | 31.90 | **31.95** |\n\nWe will polish the motivation in the revised manuscript to explicitly highlight these benefits!\n\n**W2: Concerns about the novelty**\n\nA2: Thank you for your thoughtful comment. We appreciate the opportunity to clarify the novelty and significance of our work：\n\n**1. The first attempt to bridge research gap for SNN-based SR**\n\nWhile our framework indeed integrates these components, we emphasize that it is **the first attempt** to successfully adapt and customize attention mechanisms specifically to the SNNs for SR tasks.\n\nAlthough SNNs are known for their energy efficiency, they have rarely been explored in remote sensing SR tasks due to their limited representation power compared to ANN-based models. This is primarily due to the inherent difficulty of adapting *continuous, dense* attention operations to the *discrete, sparse* spiking signals and their temporal dynamics, which pose unique architectural and training challenges.\n\nOur work is the first to mitigate this gap, achieving ANN-comparable or superior performance while maintaining significantly low energy consumption.  This novelty is recognized by Reviewer WHSi, who states our approach *makes a valuable and highly original contribution to the field* and *provides a new perspective on developing efficient models*, and Reviewer bD3C, who highlights *for the first time* and *filling the gap of SNN in the field of remote sensing SR*, further affirming the impact and novelty of our contribution.\n\n**2. Concise is not simple: Our method is concise yet highly effective**\n\nAlthough our method is concise, it is far from simplistic. Our design achieves significant performance improvements with modest architectural modifications, demonstrating that intuitive and efficient solutions can be highly effective.\nAs reported in the table below, the performance improvement demonstrates that simple yet spike-compatible attention mechanisms can improve the limited capacity of SNN by a large margin, making it competitive to ANN-based SR methods. We believe this aligns with the broader research ethos that: *a well-justified, efficient improvement is often more impactful than a complex, over-parameterized solution.* Reviewer WHSi also acknowledges that “*achieving performance comparable to or even surpassing that of ANNs using SNNs is **non-trivial**.*”\n\n| Methods | EDSR | CARN | SwinIR-light | OmniSR | NGswin | SPIN | HiT-SR | SpikeSR |\n| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n| Venus  | CVPRW'17| ECCV'28 | ICCV'21| CVPR'23| CVPR'23| ICCV'23| ECCV'24| - | \n| PSNR (dB)  | 31.64| 31.65| 31.87 | 31.91 | 31.82 | 31.82 | 31.90 | **31.95** | \n\n**3. Customized technical novelty for attention-based SNNs**\n\nWhile the design of combining SNN backbones with attention might seem straightforward, our contribution lies in the careful adaptation and customization of attention modules (HDA and DSA) to the unique spike-based characteristics. In particular:\n\n1) Our HDA and DSA operate on membrane potentials rather than dense activations, aligning with the discrete and temporal nature of spiking dynamics;\n\n2) By exploring joint temporal and channel-wise dependencies within the sparse spiking sequences, we achieve a performance improvement of +0.07dB, compared to naive temporal and channel attention mechanisms.\n\n3) Developing a pyramid structure to exploit rich multi-scale self-similarity priors. This design significantly enhances the performance against conventional spatial attention, achieving an improvement of +0.27 dB.\n\nAs reviewer WHSi notes: *our technical superiority is significant*.\n\nWe hope that the above clarifications can address your concerns, and we sincerely look forward to your updated score.\n\n**W3: Concerns about the performance**\n\nA3: Thank you for raising this valuable concern. We would like to clarify that our SpikeSR does not exhibit a performance disadvantage compared to HiT-SR. While our model has significantly fewer parameters (472K vs. 792K), it achieves a slightly lower performance (31.88 vs. 31.90), which is reasonable given the smaller model size. When comparing models with similar parameters (763K vs. 792K), our approach delivers comparable results. Furthermore, upon scaling up the model, SpikeSR outperforms HiT-SR (31.95 vs. 31.90), demonstrating the superiority and scalability of our framework.\n\nTo further highlight the favorable performance of our SpikeSR, we have conducted additional comparisons with two SOTA efficient SR methods, **SeemoRe (ICML 2024)** and **CATANet (CVPR 2025)**. As shown below, SpikeSR remains highly competitive against these cutting-edge approaches. We will add these results to our manuscript!\n\n| Methods | SeemoRe | CATANet | SpikeSR |\n| :---: | :---: | :---: | :---: |\n| AID | 30.83/0.8115 |  30.88/0.8127 | **30.91/0.8142** |\n| DOTA | 33.94/0.8671 | **33.99**/0.8688 | 30.98/**0.8700** |\n| DIOR | 30.84/0.8141 | 30.92/0.8162 | **30.95/0.8175** |\n| Average | 31.87/0.8309 | 31.93/0.8326 | **31.95/0.8339** |\n\n**W4 & W5: Missing energy consumption and memory overhead**\n\nA4 & A5: We have additionally evaluated these metrics to address this concern. As shown in the table below, our SpikeSR demonstrates **significantly lower energy consumption** compared to ANN-based methods, highlighting the inherent energy efficiency advantage of SNNs. We will include a comprehensive discussion on model efficiency in the revised manuscript!\n\n| Methods | EDSR | CARN | SwinIR-light | OmniSR | NGswin | SPIN | HiT-SR | SpikeSR-S | SpikeSR-M | SpikeSR |\n| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n| Energy (pJ)  | 162.46 | 129.25 | 70.39 | 227.14 | 40.74 | 60.51 | 67.33 | **2.96** | 4.15 | 4.74 |\n| Memory (MB)  | 202.00 | 6.00 | 126.00 | 146.00 | 10.00 | **4.00** | **4.00** | 6.00 | 10.00 | 14.00 |\n\n**Q1: Provide a more detailed theoretical analysis of the proposed SAB**\n\nA1: Thank you for raising this insightful question. We provide a more in-depth theoretical analysis of the proposed SAB. Specifically, our SAB is composed of two key modules: Hybrid Dimension Attention (HDA), which operates in *temporal-channel joint dimensions*, and Deformable Similarity Attention (DSA), which adjusts membrane potentials across *spatial dimensions*. Together, they serve to modulate the dynamics of membrane potential accumulation in a spike-compatible manner, thus improving information flow and representation quality.\n\nMathematically, in standard Leaky Integrate-and-Fire (LIF) neuron models, the membrane potential $u_t$ of neuron $i$ at time \n$t$ evolves as: $u_t^i=\\tau u_{t-1}^i+x_t^i-V_{\\mathrm{th}}s_{t-1}^i$, where $\\tau$ is the decay constant, $x_t^i$ is the input current, $s_{t-1}^i$ is the spike signal from the previous time step, and $V_{\\mathrm{th}}$ is the firing threshold. Without attention guidance, membrane potential integration is uniform, lacking selective representation in time or importance across channels and positions, which significantly limits the representation power of SNN.\n\nTo address this, we introduce attention-guided modulation over the membrane potential update: $u_t=\\tau u_{t-1}+\\alpha_t\\odot x_t+\\beta_t$. Here, $\\alpha_t\\in\\mathbb{R}^C$ is a joint temporal-channel attention map from HDA, $\\beta_t\\in\\mathbb{R}^{H\\times W}$ is a spatial modulation map from DSA. In this form, HDA re-weights input activations across channels and time, focusing accumulation on informative temporal-channel combinations. DSA refines the spatial saliency, emphasizing regions that contribute more meaningfully to super-resolution. The refined update thus becomes: $u_t^{i,j}=\\tau u_{t-1}^{i,j}+\\alpha_t^c\\cdot x_t^{i,j,c}+\\beta_t^{i,j}$. \n\nIn the backward pass, the pseudo-gradient of loss L w.r.t. the membrane potential is: $\\frac{\\partial L}{\\partial u_t}\\approx\\frac{\\partial L}{\\partial s_t}\\cdot\\sigma^{\\prime}(u_t-V_\\mathrm{th})$. Due to attention-induced focus in $u_t$, more informative gradients are passed to salient positions and time steps, yielding stronger feature representation power.\n\nWe will include this theoretical analysis in the Appendix!"}, {"review_id": "cUqy47SUjM", "reviewer": "Reviewer_Ecnp", "summary": "This 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.", "questions": "Clarification 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.", "limitations": "Yes", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "1. 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.", "was_revised": true, "final_justification": "Thank you for the authors’ rebuttal. However, after considering all the other reviews and rebuttals to them, I have decided to maintain my original score.", "rebuttal": "## **Response to Reviewer Ecnp**\n\nDear Reviewer Ecnp:\n\nThank you for your encouraging comments and insightful questions. We are glad that you found our method **technically sound**, and recognized the application of SNN to SR as **relatively novel** and **conceptually promising**. We also appreciate your recognition of the **well-articulated motivation** behind our work, and your acknowledgment that our research direction is **timely, meaningful, and worth exploring**, which highlights the originality and potential impact of our contribution. We hope that the responses below will address your concerns, and we would be truly grateful if you could consider updating the score!\n\n### Weaknesses and Questions\n\n**W1 & Q3: The paper would benefit from more ablation of time steps and connection to the underlying spiking dynamics.**\n\nA1: **To better understand the influence of time steps:** we conducted additional ablation experiments by setting different numbers of simulation steps T={1, 2, 4, 8}. As shown in the table below, increasing T allows our model to integrate more spatiotemporal cues, which leads to increased performance improvements. However, this gain saturates around T=4, suggesting that further increasing T may introduce redundant or noisy information. Moreover, a larger T also leads to additional computational overhead. Therefore, we selected T=4 as a practical trade-off between efficiency and reconstruction quality. These ablations will be included in the Appendix to strengthen the argument for the effectiveness of spike-based representations in the SR context.\n\n| Time Steps| 1| 2| 4 | 8 |\n| :---: | :---: | :---: | :---: | :---: |\n| FLOPs (G) | 32.45  | 32.78 | 33.05 | 33.31 |\n|PSNR (dB) | 27.98 | 28.10 | **28.19** | 28.17 |\n\n**To further clarify the connection between HDA and DSA modules and the spiking temporal dynamics:** we conducted an in-depth visualization of feature and spiking representations. First, HDA effectively filters out redundant spike activations along both temporal and channel dimensions, thereby enhancing the quality of feature representations, as illustrated in Fig. 7(b). Furthermore, we additionally visualized spiking maps at different time steps and observed that, with the guidance of DSA, the temporal signals exhibit notable variation across different time steps. This indicates that DSA significantly enriches the temporal dynamics and mitigates the homogenization issue commonly observed in SNNs over the temporal dimension. These visualization results and analyses will be included in the supplementary materials!\n\n**W2 & Q1: More explanation of HDA could significantly strengthen the clarity.**\n\nA2: In the revised main body of our paper, we will describe the working mechanism of the HDA module and briefly introduce its structure. Specifically, HDA first applies a squeeze operation to compress membrane potentials into a compact matrix. It then performs 1D convolutions independently along the temporal and channel dimensions to extract attention weights that reflect the dynamic importance of features across both axes. To model their interdependence, we perform element-wise multiplication to fuse the two attention weights into a unified temporal-channel attention map, which is ultimately used to modulate the membrane potential representations. Instead of operating directly on raw spike trains, HDA enhances feature representation by adaptively adjusting membrane potentials, enabling it to capture rich spatiotemporal information while preserving the discrete nature of SNNs.\n\nMoreover, we will release the source code to ensure technical completeness and reproducibility of HDA. Also, we will ensure the appendix is available and sufficiently descriptive.\n\n**W3: More investigation of spiking dynamics.**\n\nA3: We have added more visual analyses to illustrate how spike-based temporal information flow facilitates texture restoration and detail propagation. Specifically, we present visualizations of spiking maps across different time steps, which reveal that the proposed DSA module enhances the variation of spiking dynamics at each temporal stage by introducing self-similarity priors. \n\nMoreover, features across different time steps exhibit distinct levels of detail saliency, which aligns with the biological inspiration of SNNs—where the human brain selectively attends to different information at different moments. After removing the HDA module,  we observe a noticeable loss of high-frequency textures and more homogeneous patterns across time steps. These visualizations further validate the effectiveness of our model in addressing the key challenges of super-resolution.\n\n**Q2: The connection between spiking responses and actual SR reconstruction quality in Fig.1 should be clarified.**\n\nWe sincerely appreciate your recognition of Figure 1 as **an interesting perspective**. \n\nAs clarified, the high-voltage regions do not directly correspond to high-frequency areas in the final SR outputs. Rather, the visualization in Fig. 1 is intended to illustrate our motivation, not to serve as direct empirical evidence. Specifically, it demonstrates that spiking neurons can sustain an active state even when high-frequency details are reduced. In contrast, intuitively, convolutional networks tend to exhibit reduced activation in response to degraded high-frequency inputs, which may limit their representational capacity. We will revise the caption of Fig. 1 to better clarify this point and avoid potential misinterpretation."}, {"review_id": "TnGDzZ5aVY", "reviewer": "Reviewer_WHSi", "summary": "This 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.", "questions": "Please 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.", "limitations": "Yes.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "Thanks to the author of the rebuttal, and the extended materials help address my concerns. I am willing to keep my original rating of accept.", "rebuttal": "## **Response to Reviewer WHSi**\n\nDear Reviewer WHSi:\n\nWe sincerely thank the reviewer for the encouraging and insightful feedback. We are pleased that you consider our first attempt as **a valuable and highly original contribution** to the field, and our observation of active learning states **provides a new perspective** in large-scale Earth observation scenarios. We are especially encouraged by your recognition that our performance gain against ANN-based models is **non-trivial**, demonstrating a **significant technical superiority**. We hope our responses below can address your concerns.\n\n### Weaknesses\n\n**W1: Clarify the application level of deformable convolution.**\n\nA1: To save computational cost, deformable convolution is applied at the image level. This enables our model to flexibly mitigate geometric transformations across entire images while maintaining efficiency. We will clarify this detail in the revised manuscript!\n\n**W2: Please provide more ablation on different selections of T.**\n\nA2: To assess its impact, we conducted additional ablation studies with T=1,2,4,8. The results are presented below. It can be observed that as T increases, more spatiotemporal information is incorporated, leading to progressively improved performance. However, this improvement saturates when T=4. Considering that model complexity also grows with increasing T, we choose T=4 as the final setting to strike a favorable balance between performance and efficiency. This ablation will be included in the Appendix!\n\n| Time Steps| 1| 2| 4 | 8 |\n| :---: | :---: | :---: | :---: | :---: |\n| FLOPs (G) | 32.45  | 32.78 | 33.05 | 33.31 |\n|PSNR (dB) | 27.98 | 28.10 | **28.19** | 28.17 |\n\n**W3: More clarification of pixel-level intensity observed in Fig. 1a should be discussed.**\n\nA3: Yes, the smoothed pixel-level intensity indeed reflects the tendency of ANN-based architecture (e.g., convolution kernel) to over-smooth local textures, which may hinder their ability to capture fine-grained spatial details. This phenomenon highlights the importance of temporal dynamics and sparse activation in enhancing spatial sensitivity. To clarify this, we will revise the caption of Fig. 1 to explicitly point out that the smoothing effect serves as an intuitive motivation for adopting SNN architectures in SR tasks.\n\n**W4: The details of AID-tiny are unclear.**\n\nA4: Thanks for your suggestion. The AID-tiny is a lightweight dataset derived from the AID dataset, where we randomly select one image from each of the 30 categories, resulting in a total of 30 samples. These samples are non-overlapping with the training and testing sets. To ensure reproducibility, we will release the AID-tiny dataset publicly on our GitHub repository.\n\n### Questions\n\n**Q1: Some typos should be corrected.**\n\nA1: Thank you for pointing this out. We have carefully reviewed the manuscript and corrected the noted typos, including those following the values 90, 180, and 270. In addition, we have added the missing section title for Section 1. These revisions will be reflected in the updated manuscript!"}]

Author rebuttal:

Meta-review: This paper proposes an efficient remote sensing image super-resolution (SR) method based on spiking neural networks (SNNs). It combines the attention mechanism while maintaining high computational efficiency. Experiment with 30 types of remote sensing scenes shows competitive results. Even though the result method is more energy efficient, its capacity and quality is still not as good as that of ANNs.

Reviewers recognize the potential in the direction of leveraging SNNs. However, there is still concern about lack of  theoretic justification to the design in connection to SNNs. Also more comparison to strong recent methods and SNN-based Transformers will make the work more complete.
Decision: Accept (poster)