Non-contact pulse wave reconstruction hinges on the precise recovery of waveform morphology, including the dicrotic notch. Conventional Red-Green-Blue (RGB)-based methods, which extract physiological signals from recorded facial videos, are constrained by the integral imaging mechanism of standard cameras, where the exposure process induces a smoothing effect that attenuates subtle vascular pulsation details. Conversely, neuromorphic event cameras, while offering exceptional sensitivity to intensity fluctuations, are inherently susceptible to noise and artifacts induced by minor motion. To exploit the synergy between frame-based integration and event-based differential sensing, we propose a novel multimodal network named Fusion-E2Pulse. This framework utilizes filtered RGB signals as structural priors to suppress motion artifacts, while leveraging the high-sensitivity of event streams to recover fine-grained morphological details. Experimental results demonstrate that Fusion-E2Pulse achieves state-of-the-art performance, effectively balancing noise suppression and morphological fidelity, achieving a mean absolute error of 0.78 bpm for heart rate estimation, a waveform correlation of 0.89, and a systolic phase duration error of 16.74 ms, validating its efficacy in reconstructing fine-grained pathological features.

翻译：非接触式脉搏波重建的关键在于精准恢复波形形态，包括降中峡在内的特征结构。传统的基于红绿蓝（RGB）相机的方法通过录制面部视频提取生理信号，但受限于标准相机的积分成像机制——曝光过程会产生平滑效应，削弱微细血管搏动细节。相比之下，神经形态事件相机虽对强度变化具有卓越灵敏度，却易受微小运动引发的噪声和伪影干扰。为充分发挥帧基积分感知与事件差分感知的协同优势，我们提出名为Fusion-E2Pulse的新型多模态网络。该框架以滤波后的RGB信号作为结构先验抑制运动伪影，同时利用事件流的高灵敏度特性恢复精细形态细节。实验结果表明，Fusion-E2Pulse实现了当前最优性能，有效平衡了噪声抑制与形态保真度，心率估计平均绝对误差为0.78 bpm，波形相关系数达0.89，收缩期时相持续时间误差为16.74 ms，验证了其在重建精细病理特征方面的有效性。