Deep blur detection network with boundary-aware multi-scale features - Xiaoli Sun; Qiwei Wang; Xiujun Zhang; Chen Xu; Weiqiang Zhang (Connection Science)
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.