EFFICIENT DEBLURRING VIA HIGH-FREQUENCY AND LOW-FREQUENCY INFORMATION FUSION

Ruilong Lu, Yuan Yuan, Qi Wang

科研成果: 书/报告/会议事项章节会议稿件同行评审

1 引用 (Scopus)

摘要

The distortion of high-frequency information is the most fundamental problem of dynamic scene blur, which leads to the degradation of image quality. However, most deep-based methods fail to show satisfactory results because of ignoring the importance of image structural information (high-frequency and low-frequency preception) in deblurring. In this paper, we propose a high-frequency and low-frequency information fusion deblurring network (HLFNet) that uses edge perception as a guide. The proposed HLFNet consists of the high-frequency information network (HFNet) and the low-frequency information network (LFNet). Besides, we adopt the proposed multi-scale atrous convolution (MSA) block into LFNet, which can effectively reduce the number of model parameters while expanding the receptive fields. Extensive experiments show that the proposed model can achieve state-of-the-art results with smaller parameters and shorter inference time on the public datasets.

源语言英语
主期刊名2022 IEEE International Conference on Image Processing, ICIP 2022 - Proceedings
出版商IEEE Computer Society
1271-1275
页数5
ISBN(电子版)9781665496209
DOI
出版状态已出版 - 2022
活动29th IEEE International Conference on Image Processing, ICIP 2022 - Bordeaux, 法国
期限: 16 10月 202219 10月 2022

出版系列

姓名Proceedings - International Conference on Image Processing, ICIP
ISSN(印刷版)1522-4880

会议

会议29th IEEE International Conference on Image Processing, ICIP 2022
国家/地区法国
Bordeaux
时期16/10/2219/10/22

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