A novel hierarchical processing scheme is proposed to efficiently increase the spatial resolution and dynamic range of detecting particle image displacements in PIV images. The technique takes full advantage of the multi-resolution characteristic of the discrete correlation function by starting the processing at the smallest scale and, if necessary, gradually building correlation planes into larger interrogation areas based on the result of inter-level correlation correction and validation. It is shown that the algorithm can be implemented in both direct and FFT based correlation algorithms with greatly reduced computational complexity. The technique opens new perspectives for locally adaptive super-resolution processing taking flow field, seeding, and imaging anomalies into account. Processing at the lowest scale (e.g. pixel or particle image size) allows the combination of correlation planes on any shape. Hence the proposed reverse hierarchical processing represents interrogation area optimization both in size and shape in order to maximize the correlation plane signal-to-noise ratio. The method is successfully demonstrated on experimentally obtained images.