Abstract Video deblurring is a challenging low-level vision task due to variant blur artifacts caused by factors such as depth variations, high-speed movements and camera shakes. Although significant efforts have been devoted to addressing this task, two challenges of capturing temporal patterns and spatial topologies still remain. In this paper, an attention-based interframe compensation scheme is proposed to address the first challenge. The proposed scheme replaces frames in blurry sequences with newly restored frames, and estimates temporal patterns among the replaced sequence to restore the whole sequence. After each replacement, an attention block is employed to exploit dependencies among restored and blurry frames to capture stable temporal patterns. To tackle the second challenge, we propose an adaptive residual block that dynamically fuses multi-level features via learning location-specific weights. Comprehensive experimental results demonstrate that the proposed method achieves state-of-the-art performance in terms of accuracy, visual effect and model size.