Skip to main content
. 2020 May 26;11(6):3274–3287. doi: 10.1364/BOE.392699

Fig. 1.

Fig. 1.

Singular value decomposition (SVD). (a) The 3D matrix of complex-valued holograms H(x,y,t) is reshaped into a 2D space-time matrix, and decomposed in a product of 3 matrices following Eq. (1). U and V are the spatial and temporal eigenvectors, and Δ is the diagonal matrix of singular values λi. (b) Ordered singular values in dB. (c) Fourier transform magnitude of the temporal eigenvectors weighted by singular values, the arrow indicates high frequency clutter. (d) Individual or averaged spatial eigenvectors: the first vectors show clutter whereas vectors associated to singular values of lower energy reveal blood flow.