Infrared small target detection plays an important role in applications such as infrared target search and tracking. In this paper, we propose an infrared small target detection algorithm combining two-dimensional empirical modal decomposition and multi-scale patch contrast algorithm. First, the infrared image is decomposed into modal components of different scales using two-dimensional empirical modal decomposition, and then the low-frequency modal components are removed for image reconstruction to achieve the suppression of background clutter. Then, the reconstructed image is used as the input of the multi-scale patch contrast algorithm to generate the target result map. Finally, adaptive threshold segmentation is performed on the target result map to detect the real infrared small targets. The experimental simulation results show that the algorithm can effectively suppress the background interference to the target with high detection rate under different backgrounds compared with the existing algorithms, which verifies the effectiveness and robustness of the algorithm.