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.

翻译：联合图像压缩与无线传输在实际通信系统中具有重要意义，但相较于通用图像恢复领域，相关研究仍相对不足。我们将该问题建模为等效线性模型，并提出Diffusion-OAMP——一种将预训练扩散模型嵌入OAMP算法的免训练重建框架。在Diffusion-OAMP中，OAMP线性估计器生成伪AWGN观测值，而扩散模型在信噪比匹配准则下作为非线性估计器。该框架为将多种生成式先验融入OAMP提供了可行途径。不同压缩比和噪声水平下的实验表明，在评估设定中Diffusion-OAMP相较于经典方法表现更优。