The quality of a 3D map produced by the single-particle analysis method is highly dependent on an accurate assignment of orientations to the many experimental images. However, the problem's complexity implies the presence of several local minima in the optimized goal functions. Consequently, validation methods to confirm the angular assignment are very useful to yield higher-resolution 3D maps. In this work, we present a graph-signal-processing-based methodology that analyzes the correlation landscape as a function of the orientation, an approach allowing the estimation of the assigned orientations’ reliability. Using this method, we may identify low-reliability images that probably incorrectly contribute to the final 3D reconstruction.