To address the inherent limitations of traditional division-of-focal-plane polarimeters, namely low spatial resolution and an inability to acquire complete polarization information, this paper presents a hardware-software co-designed full polarization super-resolution imaging system. On the hardware side, a dual-channel, common-aperture full polarization imaging system was developed to simultaneously capture a low-resolution division-of-focal-plane linear polarization image and a high-resolution circular polarization image in a single exposure. Compared to conventional full polarization imaging systems, this design simplifies the system architecture, reduces costs, and eases assembly and alignment. To process the unique mixed-resolution data from this camera, a complementary feature-fusion-based full polarization super-resolution network was designed. This model employs a dual-branch architecture that simultaneously processes the two captured images for feature extraction. The high-resolution circular polarization image serves as precise guidance, and a feature fusion module enables effective integration of the low-and high-resolution features. To ensure feature alignment between the two branches, pixel-level registration is performed on the images from both detectors before super-resolution reconstruction, guaranteeing a spatial displacement error of less than one pixel. A loss function incorporating physical constraints is introduced to ensure the physical accuracy of reconstructed angular parameters, such as the angle of polarization and ellipticity of polarization. This process achieves high-quality reconstruction of full polarization parameters, including intensity, degree of polarization, angle of polarization, and ellipticity of polarization. On a real-world dataset, the peak signal-to-noise ratios of the four reconstructed polarization feature images improved by 0.106 dB, 0.302 dB, 0.117 dB, and 1.085 dB, respectively, while the structural similarity index measure improved by 0.002, 0.008, 0.006, and 0.014. In the field exploration experiments, this system effectively identified the unmanned aerial vehicle targets in complex scenes, verifying its significant advantages in target detection.