Blind Restoration of Images Distorted by Atmospheric Turbulence Based on Deep Transfer Learning
文献类型：期刊论文
| 作者 | Guo, Yiming1,2,3; Wu, Xiaoqing1,2,3 ; Qing, Chun1,2,3; Su, Changdong1,2,3; Yang, Qike1,2,3; Wang, Zhiyuan2,3
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| 刊名 | PHOTONICS
 |
| 出版日期 | 2022-08-01 |
| 卷号 | 9 |
| 关键词 | blind restoration images distorted by atmospheric turbulence deep transfer learning Res FFT-Conv Block high-quality images |
| DOI | 10.3390/photonics9080582 |
| 通讯作者 | Wu, Xiaoqing(xqwu@aiofm.ac.cn) |
| 英文摘要 | Removing space-time varying blur and geometric distortions simultaneously from an image is a challenging task. Recent methods (including physical-based methods or learning-based methods) commonly default the turbulence-degraded operator as a fixed convolution operator. Obviously, the assumption does not hold in practice. According to the situation that the real turbulence distorted operator has double uncertainty in space and time dimensions, this paper reports a novel deep transfer learning (DTL) network framework to address this problem. Concretely, the training process of the proposed approach contains two stages. In the first stage, the GoPro Dataset was used to pre-train the Network D1 and freeze the bottom weight parameters of the model; in the second stage, a small amount of the Hot-Air Dataset was employed for finetuning the last two layers of the network. Furthermore, residual fast Fourier transform with convolution block (Res FFT-Conv Block) was introduced to integrate both low-frequency and high-frequency residual information. Subsequently, extensive experiments were carried out with multiple real-world degraded datasets by implementing the proposed method and four existing state-of-the-art methods. In contrast, the proposed method demonstrates a significant improvement over the four reported methods in terms of alleviating the blur and distortions, as well as improving the visual quality. |
| WOS关键词 | VIDEO STABILIZATION ; INFORMATION FUSION ; ALGORITHM ; SET |
| 资助项目 | National Natural Science Foundation of China[91752103] ; Foundation of Advanced Laser Technology Laboratory of Anhui Province by Chun Qing[AHL2021QN02] |
| WOS研究方向 | Optics |
| 语种 | 英语 |
| WOS记录号 | WOS:000847202100001 |
| 出版者 | MDPI |
| 资助机构 | National Natural Science Foundation of China ; Foundation of Advanced Laser Technology Laboratory of Anhui Province by Chun Qing |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/131834]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Wu, Xiaoqing |
| 作者单位 | 1.Univ Sci & Technol China, Sci Isl Branch, Grad Sch, Hefei 230026, Peoples R China 2.Chinese Acad Sci, Hefei Inst Phys Sci, Key Lab Atmospher Opt, Hefei 230031, Peoples R China 3.Adv Laser Technol Lab Anhui Prov, Hefei 230037, Peoples R China |
| 推荐引用方式 GB/T 7714 | Guo, Yiming,Wu, Xiaoqing,Qing, Chun,et al. Blind Restoration of Images Distorted by Atmospheric Turbulence Based on Deep Transfer Learning[J]. PHOTONICS,2022,9. |
| APA | Guo, Yiming,Wu, Xiaoqing,Qing, Chun,Su, Changdong,Yang, Qike,&Wang, Zhiyuan.(2022).Blind Restoration of Images Distorted by Atmospheric Turbulence Based on Deep Transfer Learning.PHOTONICS,9. |
| MLA | Guo, Yiming,et al."Blind Restoration of Images Distorted by Atmospheric Turbulence Based on Deep Transfer Learning".PHOTONICS 9(2022). |
入库方式： OAI收割
来源：合肥物质科学研究院
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