Digital Particle Image Velocimetry (DPIV) is a digital alternative to conventional Laser Speckle Velocimetry (LSV) and Particle Image Velocimetry (PIV). DPIV uses digitally recorded video images to analyze flow velocity computationally, eliminating the need for photographic and opto-mechanical processing steps. This method resolves directional ambiguity by implementing local spatial cross-correlations between two sequential single-exposed particle images. The technique is currently limited to low-speed flows due to the 30 Hz video rate, but it offers advantages in studying unsteady phenomena like the temporal evolution of a vortex ring. The spatial velocity measurements are compared with direct measurements of particle pair separation, and the recovered velocity data are used to compute vorticity distribution and circulation. The paper discusses the mathematical foundations of deconvolution and cross-correlation in DPIV, emphasizing the use of separate images to preserve phase information and improve displacement measurement accuracy. Despite its current limitations, DPIV aims to enhance image acquisition and processing capabilities compared to PIV.Digital Particle Image Velocimetry (DPIV) is a digital alternative to conventional Laser Speckle Velocimetry (LSV) and Particle Image Velocimetry (PIV). DPIV uses digitally recorded video images to analyze flow velocity computationally, eliminating the need for photographic and opto-mechanical processing steps. This method resolves directional ambiguity by implementing local spatial cross-correlations between two sequential single-exposed particle images. The technique is currently limited to low-speed flows due to the 30 Hz video rate, but it offers advantages in studying unsteady phenomena like the temporal evolution of a vortex ring. The spatial velocity measurements are compared with direct measurements of particle pair separation, and the recovered velocity data are used to compute vorticity distribution and circulation. The paper discusses the mathematical foundations of deconvolution and cross-correlation in DPIV, emphasizing the use of separate images to preserve phase information and improve displacement measurement accuracy. Despite its current limitations, DPIV aims to enhance image acquisition and processing capabilities compared to PIV.