In preparation for Higgs factories and energy-frontier facilities, future colliders are moving toward high-granularity calorimeters to improve reconstruction quality. However, the cost and construction complexity of such detectors is substantial, making software-based approaches like super-resolution an attractive complementary tool. This study explores integrating super-resolution techniques into an LHC-like reconstruction pipeline to effectively enhance calorimeter granularity and suppress noise. We find that this software pre-processing step significantly improves reconstruction quality at fixed detector granularity, narrowing the performance gap to a finer calorimeter without closing it. To demonstrate its impact, we propose a novel transformer-based particle flow model that offers improved particle reconstruction quality and interpretability. Our results serve as a proof-of-concept, demonstrating the potential of super-resolution to enhance reconstruction in collider environments.