Imaging sensors in medium and long-wave infrared spectrum are extremely expensive. Therefore， for most consumers， remote high-resolution imaging and real-time display in these spectrums are still a challenge. This paper proposes an effective block compressed sensing method called Multi-block Combined Compressed Sensing （MBCS） adapting to Focal Plane Array Compressed Imaging system （FPA CI）， which combines parallel sampling and fast reconstruction. High-resolution images can be reconstructed from low-resolution measurement results in real-time using a low-resolution infrared sensor. The results showed that， compared with the traditional CS-based super-resolution method， this method can greatly improve the quality of the reconstructed high-resolution image and achieve a higher reconstruction speed. The optical prototype architecture and construction of the MBCS measurement matrix for the reconstruction model are also discussed. This study evaluated the reconstruction performance in terms of the block size and found that the optimal block size needs to consider both speed and reconstruction quality. Furthermore， the MBCS reconstruction algorithm with GPU acceleration was implemented to improve the image reconstruction speed of the highly parallel image system. In the experiment， the optical system and the strategy of rapid imaging and reconstruction were verified via simulation and optical experiments， which showed that the imaging speed of 512×512 resolution can reach 5 Hz.