The traditional Kalman-filtering algorithm uses batch processing to realize nonuniformity correction, and causes high computational complexity and memory requirement, and it is more important is that it cannot realize real-time processing. For this reason, a novel nonuniformity correction algorithm based on steady-state Kalman filtering is developed, which can compute the gain matrix of the filter off line according to characteristics of fixed-pattern noise and correct images by using a frame-by-frame iteration. Therefore, this algorithm has lower computational complexity and memory requirements. The fundamental of the proposed technique is described in detail and the performance is demonstrated with both simulated and real infrared imagery. Experimental results indicate that the CPU time and memory requirements that the proposed algorithm needs to correct a frame of image are 1.7188 s and 131.25 KB respectively, which are very suitable for real-time processing.