ReFlowSET是一种面向合成孔径雷达（SAR）到光电（EO）图像翻译的表征对齐潜空间流匹配框架。该框架旨在解决现有方法中自动编码器选择对重建质量影响的问题，通过联合SAR-EO重建审计选择合适的编码器，并从零开始训练一个较小的条件DiT模型，以提高SAR到EO图像翻译的准确性。
要点：
1. ReFlowSET通过联合SAR-EO重建审计选择编码器，以优化SAR到EO图像翻译的准确性。
2. 该框架从零开始训练一个条件DiT模型，以减少对预训练生成器的依赖。
3. 使用双流SAR条件化和联合特征细化来训练模型。
4. 通过将中间噪声EO特征与冻结视觉基础模型提取的清洁目标EO表示对齐，为训练提供语义指导。
5. 该框架在QXS-SAROPT和SAR2Opt数据集上实现了最先进的性能。
SAR-to-EO image translation aims to generate electro-optical (EO) imagery from synthetic aperture radar (SAR) observations. Existing latent diffusion approaches typically inherit a predetermined autoencoder, although reconstruction fidelity can vary substantially across codecs and modalities. Because the latent codec affects the round-trip preservation of both SAR conditions and EO targets, codec selection constitutes a fundamental design choice; nevertheless, existing methods largely rely on codecs pretrained on natural images. To remedy this, we introduce ReFlowSET, a conditional latent flow-matching framework that selects its codec through a joint SAR--EO reconstruction audit. Rather than inheriting a heavyweight pretrained generator, ReFlowSET trains a substantially smaller conditional DiT from scratch in the selected latent space, using dual-stream SAR conditioning followed by joint feature refinement. To provide semantic guidance for this from-scratch training, intermediate noisy-EO features are aligned with clean target-EO representations extracted by a frozen vision foundation model. This alignment is used only during training and introduces no additional inference cost. Experiments on QXS-SAROPT and SAR2Opt demonstrate state-of-the-art performance across diverse perceptual fidelity and distributional metrics. Code and pretrained weights are publicly available at https://github.com/KAIST-VICLab/ReFlowSET.
原始发布方：HuggingFace Daily Papers（社区热门论文）
原文时间：2026-09-01 08:00:00 +08:00
