Several practical applications in high-speed aerodynamics, such as the flow over wings or the engine intakes for supersonic aircraft, can be highly unsteady. Many physical mechanisms governing time-varying flow behaviour are yet to be fully established and so require investigation using optical techniques such as schlieren visualisation. Schlieren imaging is a key technique in high-speed aerodynamics, which enables flow features like shock waves to be visualised and analysed. However, the spatial and temporal resolution of the data collected is limited by the specifications of the cameras available, which can restrict the physical insight on flow unsteadiness that can be gained from the data. Novel data processing methods, which use known physics from turbulent flows or machine learning to enable the available spatial information to increase the effective temporal resolution, and vice versa. However, these methods have thus far predominantly been applied to velocity measurements and to numerical simulations, but not to schlieren imaging. Therefore, the aim of the proposed research project is to ascertain the extent to which the spatial and temporal resolution of high-speed schlieren images be improved using such data processing techniques. In order to achieve this aim, existing and novel methods will be applied to high-frequency schlieren images collected for relevant flow fields with different underlying physics – supersonic jets and boundary layers, as well as the interaction between a shock wave and the boundary layer developing on a canonical wing geometry.