用深度展开与平衡模型从人脸视频中恢复脉搏波，参数少性能强。
Recovering Pulse Waves from Video Using Deep Unrolling and Deep Equilibrium Models
- 结合信号处理与深度学习，构建反问题求解框架。
- 在公开数据集上实现顶尖心率估计精度，参数量不足竞品1/5。
- 适合需要轻量化、高鲁棒性生理信号监测的应用场景。
基于摄像头的生理信号监测（即成像光电容积脉搏波描记法，iPPG）已应用于驾驶员监控、手术中灌注评估、情感计算等多个领域。iPPG通过分析皮肤面部视频来感知心脏脉搏，并估算心率或完整脉搏波形。以往方法或采用基于模型的稀疏先验并使用迭代优化，或依赖端到端黑箱深度学习。本文提出新方法，在逆问题框架下融合信号处理与深度学习，通过学习基于深度网络的去噪算子，利用深度算法展开和深度平衡模型，从面部视频中估计潜在脉搏信号与心率。实验表明，该方法能有效去除面部信号噪声并准确推断真实心率，在知名基准测试中达到当前最优的心率估计性能，且参数量不足最接近竞争方法的五分之一。
原文摘要 · Abstract (English)
Camera-based monitoring of vital signs, also known as imaging photoplethysmography (iPPG), has seen applications in driver-monitoring, perfusion assessment in surgical settings, affective computing, and more. iPPG involves sensing the underlying cardiac pulse from video of the skin and estimating vital signs such as the heart rate or a full pulse waveform. Some previous iPPG methods impose model-based sparse priors on the pulse signals and use iterative optimization for pulse wave recovery, while others use end-to-end black-box deep learning methods. In contrast, we introduce methods that combine signal processing and deep learning methods in an inverse problem framework. Our methods estimate the underlying pulse signal and heart rate from facial video by learning deep-network-based denoising operators that leverage deep algorithm unfolding and deep equilibrium models. Experiments show that our methods can denoise an acquired signal from the face and infer the correct underlying pulse rate, achieving state-of-the-art heart rate estimation performance on well-known benchmarks, all with less than one-fifth the number of learnable parameters as the closest competing method.
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