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A lightweight-to-diffusion framework for semantic image communications
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DOI：10.1016/j.icte.2025.12.013.png)
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Abstract
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We introduce LDSeCom, a novel lightweight-to-diffusion framework for semantic image communication. LDSeCom addresses bandwidth constraints by developing LSNet, a lightweight, loop-based segmentation model at the sender, and an improved diffusion model guided by our AFM-Net at the receiver. LSNet efficiently compresses images into semantic maps, while AFM-Net’s adaptive feature modulation ensures high-quality image reconstruction. On benchmark datasets, our LSNet achieves competitive accuracy with only 0.5M parameters, while our diffusion model improves image reconstruction quality by up to 28.51% mFID. The framework enables high-fidelity results from semantic maps compressed to 1/80 of the original size, proving its efficiency for bandwidth-constrained scenarios.
Keywords:
Deep learning
Diffusion model
Lightweight-to-diffusion framework
Semantic communication
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