Abstract:

Existing video deblurring models can be categorized into two types: sliding window-based and loop-based methods. Sliding window-based methods process consecutive frames as input, limiting the clear information available to the central frame. Loop-based methods use a recurrent neural network to process each video frame sequentially, but the implicit features carrying clear information gradually weaken as the propagation distance increases. To address these issues, this article proposes a frame attention-based method—a novel video deblurring framework that differs from the two aforementioned methods by selecting the most suitable set of auxiliary frames for the central frame. To mitigate the challenge of large displacement between frames, we designed a lightweight optical flow model and fine-tuned it using distillation to enhance the alignment module’s ability to process blurred images. Experiments on two commonly used video deblurring datasets demonstrate that our method achieves a peak signal-to-noise ratio of 31.91 dB, indicating strong performance.

Key words: video deblurring, optical flow estimation, image restoration