Functional Near-Infrared Spectroscopy (fNIRS) is an emerging optical neuroimaging technology that offers a non-invasive, portable, and cost-effective method for monitoring brain activity. Aiming at the motion artifacts caused by the subjects" head movement, an improved schr?dinger filter algorithm for removing motion artifacts from fNIRS signals was proposed in combination with mathematical morphology method. The algorithm was applied to the optical density signals obtained from simulation and real experiments, reflecting the changes in hemoglobin concentration of the subjects, and its performance was compared with artifact removal algorithms such as time derivative distribution repair and kurtosis wavelet. The results show that the proposed algorithm can improve the signal-to-noise ratio of the uncorrected signal by 28.66 dB, reduce the root mean square error to 0.06, increase the square of the Pearson correlation coefficient to 0.83, and reduce the peak-to-peak error to 0.05. Compared with other algorithms, it can remove motion artifacts more effectively.