用扩散模型提升图像压缩与无线传输的重建质量。
Diffusion-OAMP for Joint Image Compression and Wireless Transmission
- 将预训练扩散模型嵌入OAMP算法，实现无训练重建。
- 在不同压缩比和噪声下表现优于经典方法。
- 适合需要高效重建且不依赖训练的通信系统。
图像压缩与无线传输的联合优化相比通用图像恢复仍研究较少，但对实际通信系统至关重要。本文基于等效线性模型构建该问题，并提出Diffusion-OAMP——一种无需训练的重建框架，将预训练扩散模型嵌入OAMP算法中。在Diffusion-OAMP中，OAMP线性估计器生成伪高斯噪声观测，而扩散模型则作为非线性估计器，在信噪比匹配规则下工作。该框架为在OAMP中融合多种生成先验提供了新途径。在不同压缩比和噪声水平下的实验表明，Diffusion-OAMP在评估设置中表现优于经典方法。
原文摘要 · Abstract (English)
Joint image compression and wireless transmission remain relatively underexplored compared to generic image restoration, despite its importance in practical communication systems. We formulate this problem under an equivalent linear model, and propose Diffusion-OAMP, a training-free reconstruction framework that embeds a pre-trained diffusion model into the OAMP algorithm. In Diffusion-OAMP, the OAMP linear estimator produces pseudo-AWGN observations, while the diffusion model serves as a nonlinear estimator under an SNR-matching rule. This framework offers a way to incorporate multiple generative priors into OAMP. Experiments with varying compression ratios and noise levels show that Diffusion-OAMP performs favorably against classic methods in the evaluated settings.
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