Particle image velocimetry (PIV) is a non-intrusive optical technique to measure thevelocity in fluid flows by relying on a tracer particles seeded inside of the flow, however this method has various issues that could compromise data acquisition and cause regions of missing data, or gaps: irregular seeding, light reflecting off surfaces, light path obstruction, poor particle choice, and imaging system restrictions. To reconstruct these missing regions, Nekkanti and Schmidt [8] developed the Gappy Spectral Proper Orthogonal Decomposition (Gappy SPOD), as a spectral counterpart to the commonly used Gappy Proper Orthogonal Decomposition (GPOD). This thesis presents improvements made to the Gappy SPOD that allow for application to raw PIV data, including a detailed gap finding method and processing of black zones (regions missing in all snapshots). We then compare this method with MF GPOD, and Nearest Neighbor (NN) interpolation in terms of their turbulent kinetic energy (TKE) error in both a global and local region capacity. We find that Nearest Neighbor often outperforms Gappy SPOD or GPOD in regions containing few missing elements. This performance suffers in regions where gap size exceeds 10^3 elements and Gappy SPOD and MF GPOD provide more accurate results. The raw data sets examined here contain greater amounts of small gaps, thus the proposed hybridization of these methods in this thesis will lead to greater accuracy.