Blur detection via deep pyramid network with recurrent distinction enhanced modules

摘要

Blur detection aims to detect the regions where the image is blur and identifies the blurred regions accurately. Since the traditional hand-crafted feature based methods usually are not robust enough to handle various complex scenarios, the study of blur detection problem remains to be a challenging task in terms of the accuracy and effectiveness of blur separation. In this paper, instead of seeking a new hand-crafted feature often seen in common approaches, we present a new deep feature-learning method by using feature pyramid network via recurrent Distinction Enhanced Block modules to solve the blur detection problem. We show that the newly introduced Distinction Enhanced Block is able to merge the high-level semantic information with the low-level details effectively, while at the same time, it is capable to keep a desirable identification between the blurry and clear regions. We also develop a new boundary penalty term to refine the boundaries of our results, which leads to more accurate detection and segmentation, compared with current available results in literature. Furthermore, due to the lacking of public datasets for blur detection problems, we have established a new blur detection dataset which may enrich the experimental benchmarks for testing. In terms of efficiency associated with the competitive performance, in particular, our model is shown to have best-in-class evaluation speed, about 0.14 s to evaluate an input image in regular personal laptop. The performance evaluations on both CUHK dataset and our SZU-BD dataset validate that the proposed model outperforms various existing state-of-the-arts methods through commonly used metric indexes. (C) 2020 Elsevier B.V. All rights reserved.