In recent years, microwave photon radar technology has received widespread attention due to its unique advantages in long-distance, high-precision measurement, and image reconstruction. This technology combines the advantages of microwave and photonics, providing an efficient solution for precise distance measurement and target recognition in complex environments. However, traditional microwave photon radar systems face significant challenges in processing large amounts of data and achieving high-resolution image reconstruction. Especially under limited observation conditions and computing resources, how to effectively improve the quality of image reconstruction and ranging accuracy, while reducing the complexity and cost of the system, has become a key research issue in this field. Compressed sensing technology, as a revolutionary signal processing method, utilizes the sparsity of signals to achieve efficient signal acquisition and reconstruction, providing a new approach to solving the above problems. In the application of microwave photon radar ranging and image reconstruction, this article will introduce compressed sensing theory, which is expected to significantly reduce the required number of samples without sacrificing measurement accuracy, thereby reducing the complexity of data processing and accelerating image reconstruction speed. Taking the image reconstruction experiment designed in this article as an example, the average peak signal-to-noise ratio of the reconstructed images in the experimental group based on the method proposed in this article is 33.17 dB, while the average peak signal-to-noise ratios of the two control groups in comparison are 26.18 dB and 24.99 dB, respectively. Through the experimental results, it can be fully seen that the method proposed in this paper can significantly improve the performance of image reconstruction.