Abstract: During the flight of a flapping-wing flying robot, the unique flapping-wing propulsion mechanism causes periodic pitching and rolling motions of the body. In passing through different airspeeds and altitudes over different terrain, the wings cycle up-and-down through predefined stroke patterns to generate the aerodynamic force required for powered flight. However, this oscillatory flapping motion also causes the aircraft structure to pitch and roll periodically about its center of mass in an oscillatory manner. Consequently, substantial high-frequency jitter shakes aerial video footage captured by the onboard optical sensors. In particular, the rapid shaking that disrupts the image is synchronized with the characteristic wing beat rhythm. This jitter negatively affects the quality and usefulness of the acquired aerial video. Repeated up-and-down pitching and rolling displacements shake aerial footage, greatly reducing its clarity and stability. Without mitigation, the jitter severely affects imaging results, limiting the potential applications of such video. To solve this problem, this paper proposes an electronic image stabilization algorithm based on oriented fast and rotated brief (ORB) and sliding mean filtering for online debounce processing. First, because the jitter period of aerial images of a flapping-wing flying robot is consistent with the wing flapping period, we designed an estimation algorithm to estimate the wing flapping period based on image features. This algorithm enables us to more accurately capture the periodic characteristics of jitter and provides important parameters for subsequent image stabilization processing. Second, we proposed a motion filtering algorithm associated with the flapping period, which can adaptively and dynamically adjust filtering parameters adaptively according to different flight conditions. The algorithm proposed in this paper is advantageous because it can dynamically adjust parameters in real time based on the actual flight conditions of flapping-wing robots, thereby better adapting to different flight conditions and further improving the image stabilization effect. Third, to verify the feasibility and stability of the algorithm, this paper performed a flight experiment by mounting a visual imaging device on a flapping-wing flying robot. Experimental results show that the proposed algorithm shows better image stabilization effects than commonly used electronic image stabilization algorithms in flapping-wing flying robots. Finally, we summarized the advantages of the proposed algorithm and provided an outlook on the future research directions.