1. Convolutional Image Correction Model for Atmospheric Turbulence Distortion.
- Author
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CHENG Kuanhong, WU Yubo, ZHU Lingjian, LI Jia, and LI Junhuai
- Subjects
ATMOSPHERIC turbulence ,ATMOSPHERIC models ,CONVOLUTIONAL neural networks ,DEEP learning ,REFRACTIVE index ,IMAGING systems - Abstract
The random fluctuations in refractive index caused by atmospheric turbulence in time and space can cause spatiotemporal blur and geometric distortion in images captured by remote imaging systems, seriously weakening the visual effect and application value of the images. In response to this issue, many scholars have attempted to use multi frame lucky zone methods and deep learning methods based on convolutional neural networks to repair image distortion caused by atmospheric turbulence. However, in the case of strong turbulence, these methods usually have greater difficulty in model training and weaker adaptive correction ability for turbulence. To address the above issues, an improved deep learning network has been proposed through research to enhance the performance of turbulence distortion correction. The network adopts a Transformer end-to-end network structure, utilizing multi head self attention to capture local contextual information across channels. At the same time, the first level correction network is trained using a Monte Carlo Dropout strategy to extract degraded regions that are difficult to capture by traditional methods through model uncertainty. Finally, using the extracted uncertainty map as guidance information, input it into the second level correction network to improve the accuracy of correction. Experiments are conducted on a synthetic turbulence degraded image set based on spatiotemporal blur and geometric distortion, demonstrating the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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