The magnetic flux leakage detection of rail surface defects will be affected by the inspection speed and other factors, which increase the background noise and reduces the detection sensitivity. In order to enhance the defect signal characteristics and improve the signal-to-noise ratio of MFL signal, a MFL signal processing method based on minimum entropy deconvolution is proposed in this paper. Through the objective function method, the optimal inverse filter parameters are calculated, and the collected magnetic flux leakage signal is processed by filtering. In order to measure the filtering effect of the minimum entropy deconvolution algorithm, the pep-to-peak values of the processed defect signals and background noise signals were compared with the wavelet transform and median filtering. The experimental results show that the minimum entropy deconvolution algorithm plays a significant role in enhancing the weak defect signal, and its effect is better than that of wavelet transform and median filtering.