Due to strict rate and reliability demands, wireless image transmission remains difficult for both classical layered designs and joint source-channel coding (JSCC), especially under low latency. Diffusion-based generative decoders can deliver strong perceptual quality by leveraging learned image priors, but iterative stochastic denoising leads to high decoding delay. To enable low-latency decoding, we propose a flow-matching (FM) generative decoder under a new land-then-transport (LTT) paradigm that tightly integrates the physical wireless channel into a continuous-time probability flow. For AWGN channels, we build a Gaussian smoothing path whose noise schedule indexes effective noise levels, and derive a closed-form teacher velocity field along this path. A neural-network student vector field is trained by conditional flow matching, yielding a deterministic, channel-aware ODE decoder with complexity linear in the number of ODE steps. At inference, it only needs an estimate of the effective noise variance to set the ODE starting time. We further show that Rayleigh fading and MIMO channels can be mapped, via linear MMSE equalization and singular-value-domain processing, to AWGN-equivalent channels with calibrated starting times. Therefore, the same probability path and trained velocity field can be reused for Rayleigh and MIMO without retraining. Experiments on MNIST, Fashion-MNIST, and DIV2K over AWGN, Rayleigh, and MIMO demonstrate consistent gains over JPEG2000+LDPC, DeepJSCC, and diffusion-based baselines, while achieving good perceptual quality with only a few ODE steps. Overall, LTT provides a deterministic, physically interpretable, and computation-efficient framework for generative wireless image decoding across diverse channels.

翻译：由于严格的速率和可靠性要求，无线图像传输对于经典分层设计和联合信源信道编码（JSCC）而言仍然具有挑战性，尤其在低延迟场景下。基于扩散的生成式解码器能够利用学习到的图像先验提供出色的感知质量，但迭代随机去噪过程会导致较高的解码延迟。为实现低延迟解码，我们提出了一种基于流匹配（FM）的生成式解码器，该解码器遵循一种新的“先着陆后传输”（LTT）范式，将物理无线信道紧密地集成到一个连续时间概率流中。对于AWGN信道，我们构建了一条高斯平滑路径，其噪声调度索引了有效噪声水平，并推导了沿此路径的闭式教师速度场。通过条件流匹配训练一个神经网络学生向量场，从而得到一个确定性的、信道感知的常微分方程（ODE）解码器，其复杂度与ODE步数呈线性关系。在推理时，它仅需估计有效噪声方差来设置ODE的起始时间。我们进一步证明，瑞利衰落和MIMO信道可以通过线性MMSE均衡和奇异值域处理，映射到具有校准起始时间的AWGN等效信道。因此，相同的概率路径和训练好的速度场无需重新训练即可复用于瑞利和MIMO信道。在MNIST、Fashion-MNIST和DIV2K数据集上，通过AWGN、瑞利和MIMO信道进行的实验表明，与JPEG2000+LDPC、DeepJSCC以及基于扩散的基线方法相比，该方法取得了持续的性能增益，同时仅需少量ODE步骤即可实现良好的感知质量。总体而言，LTT为跨多种信道的生成式无线图像解码提供了一个确定性的、物理可解释的且计算高效的框架。