Problem
Research questions and friction points this paper is trying to address.
onboard satellite
image restoration
neuromorphic accelerators
attention-free
compact encoders
LIMODENet: Attention-Free Compact Encoders for Information-Preserving Onboard Satellite Image Restoration
🤖 AI Summary
为解决卫星图像在低功耗神经形态加速器上恢复问题，提出无注意力机制的LIMODENet模型，实验证明其信息保留能力更强且更适用于实际部署。
📝 Abstract
Onboard satellites must restore a channel-degraded image on a few watts, using neuromorphic accelerators (e.g., BrainChip Akida, Intel Loihi-2) that support no softmax or attention. We ask which encoder restores best under that constraint and introduce LIMODENet (LinearMix-ODENet), a 0.69M softmax-/QKV-free backbone whose residual stages read as ODE discretizations and which is empirically information-preserving (probe accuracy rises 79.9% -> 98.4% from stem to head). At iso-parameters it restores 1 dB DVB-S2X-degraded EuroSAT better than a CNN autoencoder (+1.75 dB PSNR) and a skip-connection U-Net (+1.07 dB), three seeds, non-overlapping. Unconstrained modern restorers (NAFNet, Restormer) win on fidelity; we decompose that gap: spiking-legal additive skips recover about half, and the rest traces to attention and channel gating. LIMODENet then converts end-to-end to a spiking network with zero blocked operations, versus 22-24 for the competitors: not the best restorer available, but the best verified deployable within a real power budget.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Attention-Free
Onboard Satellite Image Restoration
Neuromorphic Accelerators
LinearMix-ODENet
Information-Preserving
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