Joint Defocus Deblurring and Superresolution Learning Network for Autonomous Driving
文献类型：期刊论文
| 作者 | Wang, Rui1,3 ; Zhang, Chunjie1,3; Zheng, Xiaolong2; Lv, Yisheng2; Zhao, Yao1,3
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| 刊名 | IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE
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| 出版日期 | 2023-09-07 |
| 页码 | 12 |
| ISSN号 | 1939-1390 |
| 关键词 | Task analysis Image reconstruction Autonomous vehicles Superresolution Cameras Visualization Transformers |
| DOI | 10.1109/MITS.2023.3307612 |
| 通讯作者 | Zhang, Chunjie(cjzhang@bjtu.edu.cn) |
| 英文摘要 | With the development of autonomous driving and computer vision, the importance of high-quality images is increasingly prominent. However, in practical applications, due to lighting, device response speed, distance, and other factors, the image captured by the onboard camera has a variety of degradations. One of the most common degradations is the combination of defocus blur and low resolution. But current image reconstruction methods are almost always used for images with single-form degradation; none of them can handle the low-resolution (LR) defocused image well. Therefore, we propose a new task for the defocus blur and LR composite situation and give a novel model: Joint Learning of Defocus Deblurring with Super-Resolution Network (J-(DSR)-S-2). This model includes two subnetworks: an auxiliary network, HR Defocus Map Estimation Network (HRDME-Net), and a main network, Super-Resolution Reconstruction Network (SRR-Net). The auxiliary net is used to predict the high-resolution (HR) defocus map and let the model understand the global defocus blur distribution, and then, the defocus map and the features of the auxiliary net are sent to the main net to assist the image reconstruction task. We verify the performance of our model on defocus blur and superresolution (SR) datasets and achieve state-of-the-art performance both quantitatively and qualitatively; the experimental results demonstrate the effectiveness of our method. |
| WOS关键词 | IMAGE ; INTELLIGENCE ; SYSTEM |
| 资助项目 | Beijing Natural Science Foundation[JQ20022] ; National Natural Science Foundation of China[62072026] ; National Natural Science Foundation of China[72225011] ; National Natural Science Foundation of China[U1936212] ; National Natural Science Foundation of China[62120106009] |
| WOS研究方向 | Engineering ; Transportation |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:001064597500001 |
| 资助机构 | Beijing Natural Science Foundation ; National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/53179]  |
| 专题 | 多模态人工智能系统全国重点实验室 |
| 通讯作者 | Zhang, Chunjie |
| 作者单位 | 1.Beijing Jiaotong Univ, Beijing Key Lab Adv Informat Sci & Network Technol, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 3.Beijing Jiaotong Univ, Inst Informat Sci, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Rui,Zhang, Chunjie,Zheng, Xiaolong,et al. Joint Defocus Deblurring and Superresolution Learning Network for Autonomous Driving[J]. IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE,2023:12. |
| APA | Wang, Rui,Zhang, Chunjie,Zheng, Xiaolong,Lv, Yisheng,&Zhao, Yao.(2023).Joint Defocus Deblurring and Superresolution Learning Network for Autonomous Driving.IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE,12. |
| MLA | Wang, Rui,et al."Joint Defocus Deblurring and Superresolution Learning Network for Autonomous Driving".IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE (2023):12. |
入库方式： OAI收割
来源：自动化研究所
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