This paper tackles the problem of motion deblurring of dynamic scenes.
Although end-to-end fully convolutional designs have recently advanced the
state-of-the-art in non-uniform motion deblurring, their performance-complexity
trade-off is still sub-optimal. Existing approaches achieve a large receptive
field by increasing the number of generic convolution layers and kernel-size,
but this comes at the expense of of the increase in model size and inference
speed. In this work, we propose an efficient pixel adaptive and feature
attentive design for handling large blur variations across different spatial
locations and process each test image adaptively. We also propose an effective
content-aware global-local filtering module that significantly improves
performance by considering not only global dependencies but also by dynamically
exploiting neighbouring pixel information. We use a patch-hierarchical
attentive architecture composed of the above module that implicitly discovers
the spatial variations in the blur present in the input image and in turn,
performs local and global modulation of intermediate features. Extensive
qualitative and quantitative comparisons with prior art on deblurring
benchmarks demonstrate that our design offers significant improvements over the
state-of-the-art in accuracy as well as speed.

该研究提出了一种用于处理动态场景运动去模糊的有效像素自适应和特征关注设计，并使用有效的内容感知全局 - 局部过滤器模块，通过动态利用相邻像素信息和全局依赖性来显著提高性能，从而 surpassed 其他先前的去模糊方法。