In consideration of the effect of global motion estimation algorithm on the real-time performance and the accuracy of a electron stabilization system, a digital image stabilization algorithm based on the improved Noble feature matching was proposed. The algorithm was composed of four steps: preselecting regions, feature matching, solving parameters and motion compensation. Firstly, the global consistency of the background pixels was used to eliminate the unreliable regions, and the adaptive threshold was added into the traditional Noble feature extraction process to ensure the features uniformly distributed in the retained image regions. Then, the neighborhood gradient information of the feature point was used to build the feature descriptors and to get the rough match sets. At the same time, the ratio of the closest neighbor to second-closest neighbor and the mean space distance criteria were both used to optimize the match sets. Furthermore, the global motion parameters were solved. Finally, the Kalman filter was used to get the true shaky components to compensate the images. Matlab experimental results indicate that the algorithm can increase the peak signal-to-noise ratio more than 2 dB, and it is especially excellent when the rotation angle is less than 5 °. In conclusion, the algorithm can stabilize video images quickly and accurately.