Thin film interferometry is a powerful technique for non-invasively measuring liquid film thickness with applications in ophthalmology, but its clinical translation is hindered by the challenges in reconstructing thickness profiles from interference patterns - an ill-posed inverse problem complicated by phase periodicity, imaging noise and ambient artifacts. Traditional reconstruction methods are either computationally intensive, sensitive to noise, or require manual expert analysis, which is impractical for real-time diagnostics. To address this challenge, here we present a vision transformer-based approach for real-time inference of thin liquid film thickness profiles directly from isolated interferograms. Trained on a hybrid dataset combining physiologically-relevant synthetic and experimental tear film data, our model leverages long-range spatial correlations to resolve phase ambiguities and reconstruct temporally coherent thickness profiles in a single forward pass from dynamic interferograms acquired in vivo and ex vivo. The network demonstrates state-of-the-art performance on noisy, rapidly-evolving films with motion artifacts, overcoming limitations of conventional phase-unwrapping and iterative fitting methods. Our data-driven approach enables automated, consistent thickness reconstruction at real-time speeds on consumer hardware, opening new possibilities for continuous monitoring of pre-lens ocular tear films and non-invasive diagnosis of conditions such as the dry eye disease.

翻译：薄膜干涉测量法是一种非侵入式测量液膜厚度的强大技术，在眼科学中具有重要应用，但其临床转化受到从干涉图样重建厚度剖面的挑战所阻碍——这是一个因相位周期性、成像噪声和环境伪影而复杂化的不适定逆问题。传统重建方法要么计算密集，要么对噪声敏感，或需要专家手动分析，难以满足实时诊断需求。为应对这一挑战，本文提出一种基于视觉Transformer的方法，可直接从孤立干涉图中实时推断薄液膜厚度剖面。通过结合生理相关合成数据与实验性泪膜数据的混合数据集进行训练，该模型利用长程空间相关性解析相位模糊，并通过单次前向传播从体内和体外获取的动态干涉图中重建时间连贯的厚度剖面。该网络在具有运动伪影的噪声、快速演化薄膜上展现出最先进的性能，克服了传统相位解缠和迭代拟合方法的局限。我们的数据驱动方法可在消费级硬件上以实时速度实现自动化、一致的厚度重建，为连续监测镜前眼表泪膜以及非侵入式诊断干眼症等疾病开辟了新前景。