Deep Image Deblurring Using Local Correlation Block

科研成果: 书/报告/会议事项章节会议稿件同行评审

2 引用 (Scopus)

摘要

Dynamic scene deblurring is a challenging problem due to the various blurry source. Many deep learning based approaches try to train end-to-end deblurring networks, and achieve successful performance. However, the architectures and parameters of these methods are unchanged after training, so they need deeper network architectures and more parameters to adapt different blurry images, which increase the computational complexity. In this paper, we propose a local correlation block (LCBlock), which can adjust the weights of features adaptively according to the blurry inputs. And we use it to construct a dynamic scene deblurring network named LCNet. Experimental results show that the proposed LC-Net produces compariable performance with shorter running time and smaller network size, compared to state-of-the-art learning-based methods.

源语言英语
主期刊名2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
出版商Institute of Electrical and Electronics Engineers Inc.
1908-1912
页数5
ISBN(电子版)9781509066315
DOI
出版状态已出版 - 5月 2020
活动2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, 西班牙
期限: 4 5月 20208 5月 2020

出版系列

姓名ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
2020-May
ISSN(印刷版)1520-6149

会议

会议2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
国家/地区西班牙
Barcelona
时期4/05/208/05/20

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