Long-range imaging in the mid-wave infrared (MWIR) is critical for defense, industrial, and environmental applications, often requiring high-resolution imaging achievable only with large-aperture lenses (~100–1000 mm). Conventional glassbased refractive optics meet these equirements but result in bulky, costly systems. While metalenses offer a lightweight alternative, creating large apertures with current fabrication techniques poses significant challenges. To address this, we employed a computational imaging approach using sparse aperture metalenses. By arranging an array of small metalenses in a spatial configuration that maximizes spatial information and applying a computational reconstruction algorithm, our system achieves high-resolution, high-contrast images equivalent to those from a single large aperture. This scalable approach allows large-aperture realization by strategically arranging smaller sub-apertures. We validated this design with a prototype of 89 mm outer diameter and 356 mm focal length. Enhanced with a neural network-based reconstruction algorithm, our proposed sparse aperture system achieves near-diffraction-limited performance. Furthermore, simulations of a large recursive sparse aperture demonstrate improved MTF coverage and higher resolution imaging. This work represents progress toward practical, scalable, high-performance MWIR imaging systems.