Cardiovascular diseases remain a pressing public health issue, necessitating the development of advanced therapeutic strategies underpinned by precise cardiac observations. While fluorescence microscopy is an invaluable tool for probing biological processes, cardiovascular signals are often complicated by persistent autofluorescence, overlaying dynamic cardiovascular entities and nonspecific labeling from tissue microenvironments. Here we present multiscale recursive decomposition for the precise extraction of dynamic cardiovascular signals. Multiscale recursive decomposition constructs a comprehensive framework for cardiac microscopy that includes pixel-wise image enhancement, robust principal component analysis and recursive motion segmentation. This method has been validated in various cardiac systems, including in vitro studies with human induced pluripotent stem cell-derived cardiomyocytes and in vivo studies of cardiovascular morphology and function in Xenopus embryos. The approach advances light-field cardiac microscopy, facilitating simultaneous, multiparametric and volumetric analysis of cardiac activities with minimum photodamage. We anticipate that the methodology will advance cardiovascular studies across a broad spectrum of cardiac models.