Dual attention per-pixel filter network for spatially varying image deblurring
文献类型：期刊论文
| 作者 | Zhang, Yanfang1; Li, Weihong1; Li, Zhenghao2; Ning, Taigong1 |
| 刊名 | DIGITAL SIGNAL PROCESSING
 |
| 出版日期 | 2021-06-01 |
| 卷号 | 113页码:17 |
| ISSN号 | 1051-2004 |
| 关键词 | Spatially varying deblurring Spatially adaptive convolution Global contextual dependence Dual attention Self-attention |
| DOI | 10.1016/j.dsp.2021.103008 |
| 通讯作者 | Li, Weihong(weihongli@cqu.edu.cn) |
| 英文摘要 | Spatially varying motion deblurring has recently witnessed substantial progress due to the development of deep neural network. However, most existing CNN-based methods involve two major shortcomings: (1) The CNN weights are space-sharing, and these methods thus ignore the properties of complex spatially variant blurs which vary from pixel to pixel in natural blurry images. (2) Stacked convolution layers with a large kernel or recurrent neural networks (RNNs) cannot capture the global contextual dependence of features, they thus cannot exploit the relationship between different blur pixels at a distance. To solve these problems, we propose a new dual attention per-pixel filter network (DAPFN). First, we develop a multiscale per-pixel filter network (MSPFN) to learn a specific deblurring mapping for different blur pixels, which predicts the per-pixel spatially adaptive convolution kernel for each blur pixel in the input blurry image of different scales and restores the clean pixel by performing channel-wise spatially adaptive convolution with the local neighborhood pixels. Second, we develop a dual attention enhanced residual network (DAERN) to capture the global contextual dependence of the blurry images, which introduces a dual attention (DA) module consisting of the spatial self-attention module (SSA) and channel self-attention module (CSA). The fusion of the two attention modules helps to further improve the deblurring performance. Third, we propose a new receptive field selection (RFS) block to learn the nonlinear characteristics of spatially variant blurs, which enables the adaptive fusing of the features with different receptive fields and effectively enhances the network nonlinear representation ability. The experimental results on GOPRO dataset indicate that the average PSNR and SSIM of the proposed method reached 31.8455 and 0.9231, respectively. The results of extensive experiments pertaining to spatially varying image deblurring demonstrate that the proposed method outperforms the state-of-the-art image deblurring methods. (C) 2021 Elsevier Inc. All rights reserved. |
| 资助项目 | Municipal Science and Technology Project of CQMMC, China[2017030502] ; Key Projects of Science and Technology Agency of Guangxi province China[Guike AA 17129002] ; Research and Application of Intelligent Video Analysis in Urban Street Occupation Management, China[20180411] |
| WOS研究方向 | Engineering |
| 语种 | 英语 |
| 出版者 | ACADEMIC PRESS INC ELSEVIER SCIENCE |
| WOS记录号 | WOS:000640937700005 |
| 源URL | [http://119.78.100.138/handle/2HOD01W0/13454]  |
| 专题 | 中国科学院重庆绿色智能技术研究院 |
| 通讯作者 | Li, Weihong |
| 作者单位 | 1.Chongqing Univ, Coll Optoelect Engn, Key Lab Optoelect Technol & Syst, Educ Minist, Chongqing 400044, Peoples R China 2.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Yanfang,Li, Weihong,Li, Zhenghao,et al. Dual attention per-pixel filter network for spatially varying image deblurring[J]. DIGITAL SIGNAL PROCESSING,2021,113:17. |
| APA | Zhang, Yanfang,Li, Weihong,Li, Zhenghao,&Ning, Taigong.(2021).Dual attention per-pixel filter network for spatially varying image deblurring.DIGITAL SIGNAL PROCESSING,113,17. |
| MLA | Zhang, Yanfang,et al."Dual attention per-pixel filter network for spatially varying image deblurring".DIGITAL SIGNAL PROCESSING 113(2021):17. |
入库方式： OAI收割
来源：重庆绿色智能技术研究院
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