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. Most existing approaches achieve a large receptive field by increasing the number of generic convolution layers and kernel size. In this work, we propose a pixel adaptive and feature attentive design for handling large blur variations across different spatial locations and process each test image adaptively. We design a content-aware global-local filtering module that significantly improves performance by considering not only global dependencies but also by dynamically exploiting neighboring pixel information. We further introduce a pixel-adaptive non-uniform sampling strategy that implicitly discovers the difficult-to-restore regions present in the image and, in turn, performs fine-grained refinement in a progressive manner. Extensive qualitative and quantitative comparisons with prior art on deblurring benchmarks demonstrate that our approach performs favorably against the state-of-the-art deblurring algorithms.

翻译：本文研究了动态场景下的运动去模糊问题。尽管端到端全卷积设计近期推动了非均匀运动去模糊领域的最新技术发展，但其性能-复杂度权衡仍非最优。现有方法大多通过增加通用卷积层数量及卷积核尺寸来扩大感受野。本研究提出了一种像素自适应与特征注意力机制，用于处理不同空间位置的大尺度模糊变化，并自适应处理每张测试图像。我们设计了一种内容感知的全局-局部滤波模块，该模块不仅考虑全局依赖关系，还能动态利用邻域像素信息，从而显著提升性能。进一步引入像素自适应非均匀采样策略，该策略能隐式发现图像中难以恢复的区域，并逐步进行精细级优化。在去模糊基准测试上，与现有技术的广泛定性与定量比较表明，本方法性能优于当前最先进的去模糊算法。