As an important pre-processing step, defocus blur detection makes critical role in various computer vision tasks. However, previous methods cannot obtain satisfactory results due to the complex image background clutter, scale sensitivity and miss of region boundary details. In this paper, for addressing these issues, we introduce a global context guided hierarchically residual feature refinement network (HRFRNet) for defocus blur detection from a natural image. In our network, the low-level fine detail features, high-level semantic and global context information are aggregated in a hierarchical manner to boost the final detection performance. In order to reduce the affect of complex background clutter and smooth regions without enough textures on the final results, we design a multi-scale dilation convolution based global context pooling module to capture the global context information from the most deep feature layer of the backbone feature extraction network. Then, a global context guiding module is introduced to add the global context information into different feature refining stages for guiding the feature refining process. In addition, by considering that the defocus blur is sensitive to image scales, we add a deep features guided fusion module to integrate the outputs of different stages for generating the final score map. Extensive experiments with ablation studies on two commonly used datasets are carried out to validate the superiority of our proposed network when compared with other 11 state-of-the-art methods in terms of both efficiency and accuracy.

作为重要的预处理步骤，散焦模糊检测在各种计算机视觉任务中起着至关重要的作用。但是，由于复杂的图像背景杂乱，缩放灵敏度和区域边界细节缺失，先前的方法无法获得令人满意的结果。在本文中，为了解决这些问题，我们引入了全局上下文指导的层次残差特征细化网络（HRFRNet），用于从自然图像进行散焦模糊检测。在我们的网络中，低级别的精细特征，高级语义和全局上下文信息以分层的方式进行聚合，以提高最终检测性能。为了减少复杂的背景杂波和平滑区域（没有足够的纹理）对最终结果的影响，我们设计了一种基于多尺度扩散卷积的全局上下文池模块，以从骨干特征提取网络的最深层特征层捕获全局上下文信息。然后，引入全局上下文指导模块，以将全局上下文信息添加到不同的特征提炼阶段，以指导特征提炼过程。此外，通过考虑散焦模糊对图像比例敏感，我们添加了一个深层功能指导的融合模块，以集成不同阶段的输出以生成最终得分图。在效率和准确性方面，与其他11种最新方法相比，我们对两个常用数据集进行了广泛的消融研究，以验证我们提出的网络的优越性。