Abstract
We engage in the relatively underexplored task named thermal infrared image enhancement. Existing infrared image enhancement methods primarily focus on tackling individual degradations, such as noise, contrast, and blurring, making it difficult to handle coupled degradations. Meanwhile, all-in-one enhancement methods, commonly applied to RGB sensors, often demonstrate limited effectiveness due to the significant differences in imaging models. In sight of this, we first revisit the imaging mechanism and introduce a Progressive Prompt Fusion Network (PPFN). Specifically, the PPFN initially establishes prompt pairs based on the thermal imaging process. For each type of degradation, we fuse the corresponding prompt pairs to modulate the model's features, providing adaptive guidance that enables the model to better address specific degradations under single or multiple conditions. In addition, a Selective Progressive Training (SPT) mechanism is introduced to gradually refine the model's handling of composite cases to align the enhancement process, which not only allows the model to remove camera noise and retain key structural details, but also enhancing the overall contrast of the thermal image. Furthermore, we introduce the most high-quality, multi-scenarios infrared benchmark covering a wide range of scenarios. Extensive experiments substantiate that our approach not only delivers promising visual results under specific degradation but also significantly improves performance on complex degradation scenes, achieving a notable 8.76\% improvement. Code is available at this https URL.
Abstract (translated)
我们专注于一个相对未被充分探索的任务，即热红外图像增强。现有的红外图像增强方法主要集中在解决单一的退化问题（如噪声、对比度和模糊），这使得处理耦合的退化变得困难。同时，通常应用于RGB传感器的一体化增强方法由于成像模型的巨大差异，在热红外成像中往往效果有限。 针对这些问题，我们首先重新审视了成像机制，并引入了一种渐进式提示融合网络（Progressive Prompt Fusion Network, PPFN）。具体而言，PPFN最初基于热成像过程建立了提示对。对于每一种退化类型，我们将相应的提示对进行融合，以调节模型的特征，从而提供自适应指导，使模型能够在单一或多种条件下降更好地应对特定退化问题。 此外，我们还引入了一种选择性渐进式训练（Selective Progressive Training, SPT）机制，逐步细化模型处理复杂情况的能力，使其增强过程更加贴合实际。这一方法不仅能使模型去除相机噪声并保留关键结构细节，还能提升热图像的整体对比度。 另外，为了验证我们的方法的有效性和通用性，我们创建了最高质量、多场景的红外基准数据集，涵盖了广泛的应用场景。 广泛的实验表明，相较于传统方法，我们的方法在处理特定退化问题时提供了视觉效果更好的结果，并且在面对复杂退化场景时表现显著提升，取得了8.76%的性能改善。代码可在以下链接获取：[此链接]（请将"[此链接]"替换为实际提供的GitHub或其他代码托管平台的链接）。
URL
https://arxiv.org/abs/2510.09343
