论文标题
基于内存的多尺度视频DeBlurring
Multi-Scale Memory-Based Video Deblurring
论文作者
论文摘要
由于深度神经网络的成功，视频DeBlurring取得了显着的进步。大多数方法通过视频序列中有限的信息传播来解决端到端的去缩合。但是，不同的框架区域表现出不同的特征，应提供相应的相关信息。为了实现细粒度的脱毛，我们设计了一个内存分支，以记住内存库中模糊的sharp特征对，从而为模糊查询输入提供了有用的信息。为了丰富我们内存库的内存，我们进一步设计了基于内存库的双向复发和多尺度策略。实验结果表明，我们的模型在保持模型的复杂性和推理时间较低的同时胜过其他最新方法。该代码可从https://github.com/jibo27/memdeblur获得。
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.
