Memory accesses are a well known bottleneck whose impact might be mitigated by using properly the memory hierarchy until registers. In this paper, we address array scalarization, a technique to turn temporary arrays into a collection of scalar variables to be allocated to registers. We revisit array scalarization in the light of the recent advances of the polyhedral model, a general framework to design optimizing program transformations. We propose a general algorithm for array scalarization, ready to be plugged in a polyhedral compiler, among other passes. Our scalarization algorithm operates on the polyhedral intermediate representation. In particular, our scalarization algorithm is parametrized by the program schedule possibly computed by a previous compilation pass. We rely on schedule-directed array contraction and we propose a loop tiling algorithm able to reduce the footprint down to the available amount of registers on the target architecture. Experimental results confirm the efficiency of our approach.