TIR-Diffusion：基于潜在域和小波域优化的扩散式热红外图像去噪
TIR-Diffusion: Diffusion-based Thermal Infrared Image Denoising via Latent and Wavelet Domain Optimization
AI总结：
针对热红外图像固定模式噪声问题，提出基于扩散模型并融合潜在域与小波域优化的去噪框架，通过级联细化增强细节，在基准和真实数据集上优于现有方法并具备零样本泛化能力。
摘要：
热红外成像在机器人感知任务中展现出巨大潜力，尤其是在能见度低或光照条件恶劣的环境中。然而，热红外图像通常受到严重的非均匀固定模式噪声的影响，使目标检测、定位和建图等任务复杂化。为解决此问题，我们提出了一种基于扩散的热红外图像去噪框架，利用潜在空间表示和小波域优化。该方法采用预训练的稳定扩散模型，通过一种结合潜在空间和离散小波变换（DWT）/双树复小波变换（DTCWT）损失的新型损失函数对模型进行微调。此外，我们实现了一个级联细化阶段以增强细节，确保高保真去噪结果。在基准数据集上的实验表明，我们的方法优于最先进的去噪方法。此外，我们的方法展现出对多样且具有挑战性的真实世界热红外数据集的稳健零样本泛化能力，突显了其在实际机器人部署中的有效性。
英文摘要：
Thermal infrared imaging exhibits considerable potentials for robotic perception tasks, especially in environments with poor visibility or challenging lighting conditions. However, TIR images typically suffer from heavy non-uniform fixed-pattern noise, complicating tasks such as object detection, localization, and mapping. To address this, we propose a diffusion-based TIR image denoising framework leveraging latent-space representations and wavelet-domain optimization. Utilizing a pretrained stable diffusion model, our method fine-tunes the model via a novel loss function combining latent-space and discrete wavelet transform (DWT) / dual-tree complex wavelet transform (DTCWT) losses. Additionally, we implement a cascaded refinement stage to enhance fine details, ensuring high-fidelity denoising results. Experiments on benchmark datasets demonstrate superior performance of our approach compared to state-of-the-art denoising methods. Furthermore, our method exhibits robust zero-shot generalization to diverse and challenging real-world TIR datasets, underscoring its effectiveness for practical robotic deployment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
