Li Kunyuan, Qi Meibin, Liu Yimin, Zhuang Shuo
Opt Lett. 2024 Sep 15;49(18):5312-5315. doi: 10.1364/OL.538600.
Recent advancements in road detection using infrared polarization imaging have shown promising results. However, existing methods focus on refined network structures without effectively exploiting infrared polarization imaging mechanisms for enhanced detection. The scarcity of datasets also limits the performance of these methods. In this Letter, we present a denoising diffusion model aimed at improving the performance of road detection in infrared polarization images. This model achieves effective integration of infrared intensity and polarization information through forward and reverse diffusion processes. Furthermore, we propose what we believe to be a novel method to augment polarized images from different orientations based on the angle of polarization. The augmented polarized image serves as the guiding condition, enhancing the robustness of the diffusion model. Our experimental results validate the effectiveness of the proposed method, demonstrating competitive performance compared to state-of-the-art methods, even with fewer training samples.
利用红外偏振成像进行道路检测的最新进展已显示出有前景的结果。然而，现有方法侧重于精细的网络结构，而没有有效地利用红外偏振成像机制来增强检测效果。数据集的稀缺也限制了这些方法的性能。在本信函中，我们提出了一种去噪扩散模型，旨在提高红外偏振图像中道路检测的性能。该模型通过正向和反向扩散过程实现了红外强度和偏振信息的有效整合。此外，我们提出了一种基于偏振角从不同方向增强偏振图像的新方法。增强后的偏振图像作为引导条件，提高了扩散模型的鲁棒性。我们的实验结果验证了所提方法的有效性，表明即使在训练样本较少的情况下，与现有最先进方法相比也具有竞争力。
