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This article proposes a novel blood volume pulse (BVP) signal extraction method for heart rate (HR) estimation that incorporates medical knowledge of the spatio-temporal BVP dynamics. Previous methods merely exploited the spatial similarity of BVPs observed from multiple facial patches and performed the low-rank approximation to extract BVP signals. If noise components are superimposed over the entire face, the previous methods have difficulty distinguishing between the BVP component and noise even in the low-rank subspace. The main novelty of the proposed method is the exploitation of the BVP characteristics in the spatial and temporal domains in a unified manner based on a dynamic mode decomposition (DMD) framework, which is used to extract spatio-temporal structures from multidimensional time-series signals. To analyze the BVP dynamics that exhibit nonlinearity and quasi-periodicity, physics-informed DMD was performed on the time-series signals extracted from facial patches in a time-delay coordinate system. This approach enables the estimation of the DMD modes, which effectively represent the spatio-temporal structures of the BVP dynamics. The other novelty of the proposed method is the incorporation of medical knowledge of the HR frequency band to select the optimal DMD mode. By incorporating this medical knowledge of HR into the proposed framework, the proposed method can accurately estimate the BVP signal and HR. The experimental results obtained using three publicly available datasets yielded a root-mean-square error of the HR estimation results of 6.37 bpm, a 36.5 % improvement over the state-of-the-art methods.

Photon counting imaging can be used to capture clearly photon-limited scenes. In photon counting imaging, information on incident photons is obtained as binary frames (bit-plane frames), which are transformed into a multi-bit image in the reconstruction process. In this process, it is necessary to apply a deblurring method to enable the capture of dynamic scenes without motion blur. In this article, a deblurring method for the high-quality bit-plane frame reconstruction of dynamic scenes is proposed. The proposed method involves the deblurring of units of object motion within a scene through the application of motion compensation to pixels sharing the same motions. This method achieves more efficient motion blur suppression than the application of simple deblurring to pixel block or spatial region units. It also applies a novel technique for accurate motion estimation from the bit-plane frame even in photon-limited situations through the statistical evaluation of the temporal variation of photon incidence. In addition to deblurring, our experimental results also revealed that the proposed method can be applied for denoising, which improves the peak signal-to-noise ratio by 1.2 dB. In summary, the proposed method for bit-plane reconstruction achieves high quality imaging even in photon-limited dynamic scenes.