2026· IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing· Vol 19, pp. 26397-26413· 0 citations· 52 references
TL;DR
Results indicate that SenSNN provides a complementary low-energy architecture for dense tiny-object screening under constrained operation-level energy budgets, and shows that localized spiking consensus provides a more efficient tradeoff than global attention for subpixel targets.
Abstract
Real-time monitoring on nanosatellites is constrained by limited power and computation. While convolutional neural networks achieve strong performance in terrestrial vision tasks, their dense multiply-accumulate (MAC) operations make them costly for continuous onboard screening. To address this challenge, we propose SenSNN, a hardware-aware spiking neural network for remote-sensing tiny-object pseudo-mask segmentation. SenSNN introduces a dual-stream membrane shortcut to preserve subthreshold responses of microtargets and mitigate feature decay in deep spiking layers. It also employs a localized multiscale temporal consensus module to suppress redundant background spikes and improve the accuracy–energy tradeoff. We analyze SenSNN through a Pareto-oriented design space study, showing that localized spiking consensus provides a more efficient tradeoff than global attention for subpixel targets. Since SODA-A provides instance-level polygon annotations rather than native semantic segmentation masks, all SODA-A experiments in this work are conducted under a polygon-derived binary foreground pseudo-mask segmentation protocol. Under this protocol, SenSNN achieves 21.87% mIoU in its high-accuracy configuration. Its efficiency-oriented configuration achieves 20.87% mIoU and is estimated to require 4.18 mJ per inference under a 45-nm operation-level MAC/accumulate energy model, providing an approximately 4× estimated arithmetic-energy reduction compared with the resolution-matched MobileNetV2-UNet baseline (20.83% mIoU and 16.9 mJ). Additional supervised adaptation experiments on AI-TOD and DOTA-v1.0 further support the applicability of SenSNN on independent remote-sensing benchmarks. These results indicate that SenSNN provides a complementary low-energy architecture for dense tiny-object screening under constrained operation-level energy budgets.
This work pioneers the synergistic integration of SNNs into Transformer architectures for LLIE, establishing a compelling pathway toward powerful, energy-efficient low-level vision on resource-constrained platforms.
Hongzhi Wang, Xiubo Liang, Jinxing Han et al.· Advances in Neural Informati...· 0 citations
To address the challenges of high parameter redundancy and prohibitive computational complexity inherent in traditional convolutional neural networks and Transformer architectures—which impede deployment on resource-constrained edge medical devices—this paper proposes LightVM-SparseUNet, an ultra-lightweight medical image segmentation framework based on state space models. The core innovations are twofold: First, a Multi-path Visual Mamba module is designed to significantly enhance feature extraction efficiency via a linear-complexity inference mechanism while maintaining feature channel integrity. Second, a sparse-sampling self-attention mechanism is integrated into the U-shaped skip connections, enabling the precise capture of long-range spatial dependencies and mitigating spatial information loss at minimal computational cost. Experimental results demonstrate that LightVM-SparseUNet achieves segmentation competitive with state-of-the-art large-scale models across two authoritative public datasets. Critically, the proposed model achieves extreme lightweights, with a parameter count of only 0.08 M and a computational overhead of merely 0.16 GFLOPs.Our method is highly practical, and the code can be found at https://github.com/yjzbkl/LightVM-SparseUNet.
Haojie Fan, Kang Xu, Xiaoyu Hou et al.· Biomedical engineering and p...· 0 citations
Semantic segmentation is critical for intelligent robotics to understand complex environments. While CNN-based models on RGB images achieve high performance, their accuracy drops in fast-motion or low-light scenes. Fortunately, event cameras, with high temporal resolution and low latency, offer robust perception in such challenging conditions. Many event-image fusion methods attempt to combine the complementary strengths of both modalities, but most adopt simple fusion strategies without considering intermodal correlations or designing computationally expensive architectures, resulting in degraded accuracy and high energy costs. To overcome these limitations, we propose a lightweight spiking neural network (SNN)-based event-image fusion network (Spike-EIFNet) that leverages the complementary strengths of multimodal fusion and energy-efficient spike-driven computation. In particular, to reduce computation cost for lightweight, Spike-EIFNet adopts a dual-branch SNN encoder to process events and images in parallel. Then, to improve the segmentation accuracy with enhanced feature interaction, we introduce a spike-driven cross-modal fusion (SCMF) module, consisting of a modality-aware fine-grained extraction (MFE) stage to capture dynamic cues from events and spatial details from images, followed by a cross-modal interaction and fusion (CIF) stage for effective feature alignment. Finally, a lightweight feature enhancement (LFE) module is proposed to further refine feature representations and facilitate deep-shallow feature fusion. Extensive experiments demonstrate that Spike-EIFNet achieves 67.34% and 58.09% mean intersection over union (mIoU) on the DDD17 and DSEC-Semantic datasets while consuming $72.83\times $ and $100.26\times $ less energy, respectively. Compared with ANN-based methods, Spike-EIFNet significantly reduces energy consumption; among SNN-based methods, it achieves the highest segmentation accuracy with a favorable accuracy-efficiency tradeoff. Code is available at: https://github.com/Chensyfighting/Spike-EIFNet.
Siyu Chen, Qie Liu, Xianlei Long et al.· IEEE Transactions on Neural...· 0 citations
Quantitativi laser speckle contrast imaging (LSCI) is constrained by the frame rate bottlenecks of conventional cameras, making it difficult to satisfy the demands of ultra-high-speed and real-time flow velocity measurements. Event cameras offer a high-dynamic optical perception paradigm with microsecond-level temporal resolution. However, existing spatiotemporal autocorrelation algorithms based on event streams incur massive computational overheads, restricting their advancement towards real-time intraoperative monitoring. This paper proposes a lightweight hybrid Spiking Neural Network and Convolutional Neural Network (SNN-CNN) computational imaging framework. By employing sparse voxelisation, this framework directly extracts high-frequency features from asynchronous event streams, achieving a low-latency end-to-end inference of 432ms. Furthermore, to address the block artefacts in heatmaps at microscopic scales, a dual transposed convolution architecture is introduced for algorithmic compensation, effectively restoring a continuous and smooth flow velocity field that complies with physical laws. Translation experiments using a frosted glass slide demonstrate that the system exhibits high quantitative velocity measurement accuracy and spatial robustness across a wide range of flow velocities, providing a novel computational imaging approach and engineering paradigm for real-time quantitative optical measurement under extreme conditions.
Mai Zhang, Zeren Gao, Yu Fu· International Conference on...· 0 citations
Experiments on HDRTV1K show that Bio-SFT achieves competitive perceptual quality and consistently improves HDR-VDP-3 and $\Delta E_{ITP}$ while reducing artifact propagation in symmetric guidance pipelines.
Tingyu Cheng, Ting Zhang, Chongyi Li et al.· 0 citations
ANN-based All-in-One image restoration (AiOIR) unifies diverse degradation handling but incurs high computational costs, limiting its real-time deployment. While Spiking Neural Networks (SNNs) offer a low-power alternative, applying them to static images remains challenging. This difficulty arises because explicit event signals are absent, and degradation cues are heavily entangled with scene structures, hindering the learning of reliable restoration-oriented spike events. To address these issues, we propose SpikeRestormer, an energy-efficient SNN for AiOIR that performs event reasoning over internally generated spike cues. Specifically, we propose a degradation-event perception process to extract spike-based degradation events through Subtractive Degradation Event Attention (SDEA). Moreover, we introduce Hierarchical Bayesian Skip Masking (HBSM) and Additive Restoration Event Attention (AREA) processes for event-reliability inference and restoration-event construction, respectively. By integrating these complementary processes, SpikeRestormer formulates restoration as a unified process of degradation-event perception, degradation-event reliability inference, and restoration-event construction, liberating the potential of SNNs for energy-efficient AiOIR. Extensive experiments show that SpikeRestormer delivers competitive performance against ANN-based methods and establishes new state-of-the-art results among SNN-based methods with significantly lower energy consumption.
Shengkai Hu, Jie Shao, Jiaqi Ma et al.· 1 citation