# FlowSem: Flow Matching for Adaptive Wireless Image Transmission in Semantic Communication

arXiv: https://arxiv.org/abs/2608.21651 · HTML: https://mlsanity.com/paper/2608.21651/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Diffusion for compression and wireless, one of 14 directions in Diffusion models.
- Builds on 8 of its 48 references that are in the map.
- First posted on arXiv 21 Aug 2026.

## Placed in

- Diffusion models / [Diffusion for compression and wireless](https://mlsanity.com/diffusion/diffusion-for-compression-and-wireless/)

## Builds on (most cited first, 8 of 8)

- [I2SB: Image-to-Image Schr"odinger Bridge](https://mlsanity.com/paper/2302.05872/) (2023)
- [PnP-Flow: Plug-and-Play Image Restoration with Flow Matching](https://mlsanity.com/paper/2410.02423/) (2024)
- [Semantic Communications: Principles and Challenges](https://mlsanity.com/paper/2201.01389/) (2021)
- [Generative Joint Source-Channel Coding for Semantic Image Transmission](https://mlsanity.com/paper/2211.13772/) (2022)
- [High Perceptual Quality Wireless Image Delivery with Denoising Diffusion Models](https://mlsanity.com/paper/2309.15889/) (2023)
- [Semantics-Guided Diffusion for Deep Joint Source-Channel Coding in Wireless Image Transmission](https://mlsanity.com/paper/2501.01138/) (2025)
- [HazeMatching: Dehazing Light Microscopy Images with Guided Conditional Flow Matching](https://mlsanity.com/paper/2506.22397/) (2025)
- [Low-Latency Generative Semantic Communication via Channel-Realization Flow Matching](https://mlsanity.com/paper/2607.24876/) (2026)

## Abstract (arXiv)

Wireless image transmission becomes challenging under poor channel conditions and stringent bandwidth constraints, as the receiver needs to preserve both pixel-level fidelity and meaningful visual structure. Classical separation-based systems, such as better portable graphics with low-density parity-check coding (BPG+LDPC), may suffer from cliff-effect behavior. While deep joint source-channel coding (DeepJSCC) provides graceful degradation as channel conditions worsen, its reconstructions may lose fine details under strong compression and severe channel distortion. To address this limitation, this paper proposes a two-stage flow matching-based semantic communication framework, termed FlowSem. In the first stage, a signal-to-noise ratio (SNR)-adaptive DeepJSCC model maps the source image into channel symbols and produces a coarse reconstruction at the receiver. In the second stage, a conditional flow matching model generates the final image from Gaussian noise conditioned on the DeepJSCC reconstruction and channel SNR. The proposed framework is evaluated on the Cityscapes dataset under additive white Gaussian noise (AWGN) and Rayleigh fading channels using a fixed channel-symbol budget. The baselines include a rate-matched BPG+LDPC system, DeepJSCC, a denoising diffusion probabilistic model (DDPM), and a denoising diffusion implicit model (DDIM). Results show that FlowSem achieves competitive pixel-level fidelity and improved structural and perceptual reconstruction quality over the considered generative baselines across different channel conditions. FlowSem provides up to 60% lower Fr'echet Inception Distance (FID) than the diffusion baselines at low SNRs. Moreover, FlowSem reaches high reconstruction quality using only a few ODE integration steps, providing a favorable quality-latency tradeoff compared with standard DDPM and accelerated DDIM sampling.