Nonuniformity correction is a critical task for achieving higher performances in modern infrared imaging systems. Lately, special interest has been given to a scene-based adaptive nonuniformity correction approach based on a neural network with a steepest descent learning rule. However, low motion and some scene artifacts such as edges usually cause the production of ghosting-like artifacts over the output images. We assume that such ghosting is mainly produced by the use of fixed learning rates. In this work we propose the addition of two adaptive learning rate strategies to minimize the presence of ghosting artifacts. The first proposal relies in the fact that a momentum term can accelerate and stabilize a learning process, which in this case could lead to a possible ghosting reduction as well. As an alternative to the momentum, we also propose the use of a variable learning rate related to the local variance of the input image. The both proposed improvements are tested in infrared image sequences with simulated nonuniformity. Results demonstrate that the proposed approaches are helpful in increasing the quality of the corrected image sequences. Nonetheless, only the variable learning rate seems to reduce in a better way the unwanted ghosting artifacts.