Application of inexpensive light emitting diodes as backlight sources for time-resolved shadowgraph imaging is demonstrated. The two light sources tested are able to produce light pulse sequences in the nanosecond and microsecond time regimes. After determining their time response characteristics, the diodes were applied to study the gas bubble formation around laser-heated copper nanoparticles in superfluid helium at 1.7 K and to determine the local cavitation bubble dynamics around fast moving metal micro-particles in the liquid. A convolutional neural network algorithm for analyzing the shadowgraph images by a computer is presented and the method is validated against the results from manual image analysis. The second application employed the red-green-blue light emitting diode source that produces light pulse sequences of the individual colors such that three separate shadowgraph frames can be recorded onto the color pixels of a charge-coupled device camera. Such an image sequence can be used to determine the moving object geometry, local velocity, and acceleration/deceleration. These data can be used to calculate, for example, the instantaneous Reynolds number for the liquid flow around the particle. Although specifically demonstrated for superfluid helium, the technique can be used to study the dynamic response of any medium that exhibits spatial variations in the index of refraction.