We propose a phase retrieval approach that uses correlation-based measurements with
compactly supported measurement masks.  The algorithm admits deterministic measurement constructions together with a robust, fast recovery algorithm that consists of solving a system of linear equations in a lifted space, followed by finding an eigenvector (e.g., via an inverse
power iteration).  Theoretical reconstruction error guarantees are presented.  Numerical experiments demonstrate  robustness  and  computational  efficiency  that  outperforms  competing  approaches  on  large
problems.  Finally,  we show that this approach also trivially extends to phase retrieval problems
based on windowed Fourier measurements.