Scene-based adaptive nonuniformity correction (NUC) is currently being applied to achieve higher performance in infrared imaging systems. However, almost all scene-based NUC algorithms cause the production of ghosting artifacts over output images. Based on constant-statistics theory, we propose a novel threshold self-adaptive ghosting reduction algorithm to improve the space low-pass and temporal high-pass (SLPTHP) NUC technique. The correction parameters of the previous frame are regarded as thresholds to compute new correction parameters. Experimental results show that the proposed algorithm can obtain a satisfactory performance in reducing unwanted ghosting artifacts.