Abstract
A blur detection problem which aims to separate the blurred and clear regions of an image is widely used in many important computer vision tasks such object detection, semantic segmentation, and face recognition, attracting increasing attention from researchers and industry in recent years. To improve the quality of the image separation, many researchers have spent enormous efforts on extracting features from various scales of images. However, the matter of how to extract blur features and fuse these features synchronously is still a big challenge. In this paper, we regard blur detection as an image segmentation problem. Inspired by the success of the U-net architecture for image segmentation, we propose a multi-scale dilated convolutional neural network called MSDU-net. In this model, we design a group of multi-scale feature extractors with dilated convolutions to extract textual information at different scales at the same time. The U-shape architecture of the MSDU-net can fuse the different-scale texture features and generated semantic features to support the image segmentation task. We conduct extensive experiments on two classic public benchmark datasets and show that the MSDU-net outperforms other state-of-the-art blur detection approaches.
Original language | English |
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Article number | 1873 |
Pages (from-to) | 1-13 |
Number of pages | 13 |
Journal | Sensors (Switzerland) |
Volume | 21 |
Issue number | 5 |
DOIs | |
State | Published - Mar 1 2021 |
Externally published | Yes |
Funding
Funding: This work was supported by the National Natural Science Foundation of China (No. 61403291 and No. 61901341). Acknowledgments: This work was supported by the National Natural Science Foundation of China (No. 61403291 and No. 61901341). The authors would like to thank Professo Wei Shao for critically reviewing the manuscript. The experiments were performed at the Deep Intelligence Laboratory of Xidian University. It is gratefully acknowledged for the persons’ help of laboratory.
Keywords
- Blur detection
- Image segmentation
- U-shaped network