  Video deblurring has achieved remarkable progress thanks to the success of
deep neural networks. Most methods solve for the deblurring end-to-end with
limited information propagation from the video sequence. However, different
frame regions exhibit different characteristics and should be provided with
corresponding relevant information. To achieve fine-grained deblurring, we
designed a memory branch to memorize the blurry-sharp feature pairs in the
memory bank, thus providing useful information for the blurry query input. To
enrich the memory of our memory bank, we further designed a bidirectional
recurrency and multi-scale strategy based on the memory bank. Experimental
results demonstrate that our model outperforms other state-of-the-art methods
while keeping the model complexity and inference time low. The code is
available at https://github.com/jibo27/MemDeblur.
