Blood flow changes within the human tissue have two main characteristics- i) the temporal pulsatile variation caused by the regular heart beat, and ii) the spatial variation that captures the presence of blood vessels beneath the skin surface. Hemodynamics, which describes blood flow throughout the body, is important to monitor for many medical conditions. The use of visible and near-infrared light for deep tissue hemodynamics imaging is emerging as a low-cost and safe alternative to some of the existing state-of-the-art technologies. However, the best camera-based systems suffer from a poor signal-to-noise ratio and low signal contrast regime due to light interaction in the tissue. Hence, accurate estimation using light-based imaging remains an open problem.

In this thesis, we develop two camera-based systems to estimate two dimensions of tissue hemodynamics: i) heart rate as a measure of temporal variation, and ii) deep tissue perfusion to map spatial variation across the tissue. In the first part, we present RobustPPG, a camera-based, motion-robust imaging technique for estimating heart rate accurately from human face videos under normal ambient illumination. We explicitly model and generate motion distortions due to the movements of the person's face using inverse rendering. The generated motion distortion is then used to filter the motion-induced measurements. We demonstrate that our approach performs better than the state-of-the-art methods in extracting a clean blood volume signal with over 2 dB signal quality improvement and 30% improvement in RMSE of estimated heart rate in intense motion scenarios.

In the second part of our thesis, we present SpeckleCam, a camera-based system to recover deep tissue blood perfusion in high resolution. We use a line scanning system and a fast algorithm for recovering high-resolution blood flow deep inside the tissue. Our approach replaces the traditional matrix-multiplication form with a convolution-based forward model that enables us to develop an efficient and fast blood flow reconstruction algorithm, with 10× speedup in runtime compared to the prior methods. We show that our proposed approach can recover complex structures 6 mm deep inside a tissue-like scattering medium in the reflection geometry. We also demonstrate the sensitivity of our approach to real tissue to detect reduced flow in major blood vessels during arm occlusion.