Recently, blur detection is a hot topic in computer vision. It can accurately segment the blurred areas from an image, which is conducive for the post-processing of the image. Although many hand-crafted features based approaches have been presented during the last decades, they were not robust to the complex scenarios. To solve this problem, we newly establish a boundary-aware multi-scale deep network in this paper. First, the VGG-16 network is used to extract the deep features from multi-scale layers. Contrast layers and deconvolutional layers are added to make the difference between the blurred areas and clear areas more prominent. At last, a new boundary-aware penalty is introduced, which makes the edges of our results much clearer. Our method spends about 0.2 s to evaluate an image. Experiments on the large dataset confirm that the proposed model performs better than other models.

最近，模糊检测是计算机视觉领域的热门话题。它可以准确地从图像中分割出模糊区域，有利于图像的后期处理。尽管在过去的几十年中已经提出了许多基于手工特征的方法，但它们对于复杂的场景并不稳健。为了解决这个问题，我们在本文中新建立了一个边界感知的多尺度深度网络。首先，使用 VGG-16 网络从多尺度层中提取深层特征。添加了对比层和反卷积层，使模糊区域和清晰区域之间的差异更加突出。最后，引入了一种新的边界感知惩罚，这使得我们结果的边缘更加清晰。我们的方法花费大约 0.2 秒来评估图像。