通过可学习的残差去噪提升图像在噪声信道下的语义通信质量。
Learnable Residual-Based Latent Denoising in Semantic Communication
- 用迭代残差学习构建可训练的潜在空间去噪映射。
- 根据信道信噪比预测去噪相似度，动态调整去噪步数。
- 降低通信延迟，实现不同噪声水平下的高效图像重建。
提出一种基于潜在空间去噪的语义通信框架，用于在噪声信道中可靠传输图像。通过在接收端引入可学习的潜在去噪器，对收到信号进行预处理，有效消除信道噪声并恢复语义信息，从而提升解码图像质量。具体而言，采用迭代残差学习方法建立潜在去噪映射，提升去噪效率并保证性能稳定。此外，利用信道信噪比（SNR）估计并预测潜在相似度（SS），根据预测的SS序列自适应调整去噪步数，进一步降低通信延迟。仿真结果表明，该框架可在多种噪声水平下有效且高效地去除信道噪声，重建视觉效果良好的图像。
原文摘要 · Abstract (English)
A latent denoising semantic communication (SemCom) framework is proposed for robust image transmission over noisy channels. By incorporating a learnable latent denoiser into the receiver, the received signals are preprocessed to effectively remove the channel noise and recover the semantic information, thereby enhancing the quality of the decoded images. Specifically, a latent denoising mapping is established by an iterative residual learning approach to improve the denoising efficiency while ensuring stable performance. Moreover, channel signal-to-noise ratio (SNR) is utilized to estimate and predict the latent similarity score (SS) for conditional denoising, where the number of denoising steps is adapted based on the predicted SS sequence, further reducing the communication latency. Finally, simulations demonstrate that the proposed framework can effectively and efficiently remove the channel noise at various levels and reconstruct visual-appealing images.
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