The super-resolution reconstruction algorithm is an algorithm that restores low-resolution images to high-resolution images, which is widely applied in the fields of medicine, remote sensing, military security, and face recognition. It is hard to construct datasets in some specific scenarios, such that the application of super-resolution reconstruction algorithms based on deep learning is limited. The scanning pattern of micro-scanning imaging technology is fixed, which requires high precision of the device. To address these two problems, we propose an image super-resolution reconstruction algorithm based on active displacement imaging. Specifically, we control the camera to move randomly while recording the displacement at the sampling moment and then reconstruct the high-resolution images by solving, mapping, and selecting zones, obtaining the sub-pixel information between multiple frames, and finally iteratively updating the reconstruction. The experimental results show that this algorithm outperforms the latest multi-featured super-resolution reconstruction algorithms for POCS images in terms of PSNR, SSIM, and mean gradient. What's more, the present algorithm does not require a fixed scanning pattern, which reduces the requirement of the micro-scanning technique on the device in place accuracy.