The performance of abnormal event detection has been greatly improved in recent years. However, due of the problems of low resolution and few labeled training samples, it is difficult to apply in practice. To address these issues, we propose a few-shot scene-adaptive abnormal event detection method. First, to restore high-quality video from low-quality video, we propose a weighted recurrent backprojection network (WRBPN). WRBPN selectively extracts supplementary information from adjacent frames based on their similarity, which is beneficial to the reconstruction of the target frame and suppresses the interference of irrelevant frames. Second, we propose an abnormal event detection network to detect abnormal events in high-quality surveillance videos. The network extracts multiscale features to obtain more discriminative representations and uses variance attention to suppress the interference of background noise. On this basis, the video frame is predicted, and abnormal event detection is performed according to the prediction error. Finally, in order to quickly adapt the model to new scenarios, we propose a metalearning-based approach. During the training process, the distribution of model parameters is learned, and the specific parameters of the model are obtained via distribution sampling. Experiments on benchmark datasets demonstrate the effectiveness of our method.