Digital particle image velocimetry (DPIV) is a digital counterpart of conventional laser speckle velocimetry (LSV) and particle image velocimetry (PIV). It uses digitally recorded video images, eliminating photographic and opto-mechanical processing. Directional ambiguity is resolved by local spatial cross-correlation between two sequential images. Images are recorded at video rate (30 Hz or slower), limiting application to low-speed flows. Sequential imaging allows study of unsteady phenomena, such as the temporal evolution of a vortex ring. Velocity data are compared with direct particle separation measurements. Recovered data compute vorticity and circulation. Conventional PIV and LSV methods track particles, but offer higher resolution. LSV uses dense particle concentrations to create speckle, while PIV uses sparse particles. Both rely on photographic images and laser interrogation. Fourier transforms are used to measure fringe spacing and angle, corresponding to particle displacement. An alternative is optical correlation. LSV was first applied to fluid flows by Barker and Fourney (1977) and Grousson and Mallick (1977). PIV has been used in various experiments, including turbulent wakes and cavitation bubbles. PIV and LSV are time-consuming due to opto-mechanical techniques. Overlapping particles in low-velocity regions cannot yield velocity vectors without image shifting. This reduces spatial resolution. PIV and LSV lose phase information, causing directional ambiguity. Image shifting or color coding can resolve this, but complicates experiments. Blackwelder et al. (1988) digitized photographic records and cross-correlated images. Video-based techniques, such as frame-to-frame cross-correlation, have been proposed. Cho (1989) formulated equations for digital Young's fringe method. Kimura and Takamori (1986) used frame-to-frame cross-correlation to study unsteady flows. Their method had limited resolution, while DPIV achieves sub-pixel accuracy. DPIV uses video technology to improve image acquisition and processing. Current limitations are technological, not conceptual. The maximum image acquisition rate of 30 Hz limits application to low-speed flows. DPIV emphasizes video-related technology for better image processing. While PIV currently offers better spatial resolution for high-speed flows, DPIV's limitations are technological. The next section discusses deconvolution and cross-correlation techniques in DPIV.Digital particle image velocimetry (DPIV) is a digital counterpart of conventional laser speckle velocimetry (LSV) and particle image velocimetry (PIV). It uses digitally recorded video images, eliminating photographic and opto-mechanical processing. Directional ambiguity is resolved by local spatial cross-correlation between two sequential images. Images are recorded at video rate (30 Hz or slower), limiting application to low-speed flows. Sequential imaging allows study of unsteady phenomena, such as the temporal evolution of a vortex ring. Velocity data are compared with direct particle separation measurements. Recovered data compute vorticity and circulation. Conventional PIV and LSV methods track particles, but offer higher resolution. LSV uses dense particle concentrations to create speckle, while PIV uses sparse particles. Both rely on photographic images and laser interrogation. Fourier transforms are used to measure fringe spacing and angle, corresponding to particle displacement. An alternative is optical correlation. LSV was first applied to fluid flows by Barker and Fourney (1977) and Grousson and Mallick (1977). PIV has been used in various experiments, including turbulent wakes and cavitation bubbles. PIV and LSV are time-consuming due to opto-mechanical techniques. Overlapping particles in low-velocity regions cannot yield velocity vectors without image shifting. This reduces spatial resolution. PIV and LSV lose phase information, causing directional ambiguity. Image shifting or color coding can resolve this, but complicates experiments. Blackwelder et al. (1988) digitized photographic records and cross-correlated images. Video-based techniques, such as frame-to-frame cross-correlation, have been proposed. Cho (1989) formulated equations for digital Young's fringe method. Kimura and Takamori (1986) used frame-to-frame cross-correlation to study unsteady flows. Their method had limited resolution, while DPIV achieves sub-pixel accuracy. DPIV uses video technology to improve image acquisition and processing. Current limitations are technological, not conceptual. The maximum image acquisition rate of 30 Hz limits application to low-speed flows. DPIV emphasizes video-related technology for better image processing. While PIV currently offers better spatial resolution for high-speed flows, DPIV's limitations are technological. The next section discusses deconvolution and cross-correlation techniques in DPIV.