Non-uniformity is a common inherent defect in infrared imaging systems, mainly manifesting as streaks and granular fixed-pattern noise that severely degrade image quality. Current scene-based non-uniformity correction methods generally rely on moving scenes, and stationary scenes are easily assumed to be non-uniform, resulting in incorrect correction and ghosting artifacts. To solve the problem, the scene assumption was discarded to correct the large-scale strike non-uniformity based on one-dimensional guided filtering combined with progressive correction and energy blocking mechanism from the characteristics of non-uniformity. On the temporal low-pass image, based on the scale awareness and convergence properties of the rolling guidance filter, the small-scale scatter non-uniformity was corrected by solving the parameters of the linear correction model under the minimum mean square error. To verify the robustness of the algorithm, simulations and real experiments were performed on motionless scenes, and through subjective and objective evaluations, it was verified that this algorithm had the advantages of small non-uniformity correction residuals, strong ability to inhibit ghosting, no over-correction, no dependence on motion scenes, and had the ability to correct both video streams and single frame images.