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Deblurring Ghost Imaging Reconstruction Based on Underwater Dataset Generated by Few-Shot Learning.
- Source :
-
Sensors (14248220) . Aug2022, Vol. 22 Issue 16, p6161-6161. 20p. - Publication Year :
- 2022
-
Abstract
- Underwater ghost imaging based on deep learning can effectively reduce the influence of forward scattering and back scattering of water. With the help of data-driven methods, high-quality results can be reconstructed. However, the training of the underwater ghost imaging requires enormous paired underwater datasets, which are difficult to obtain directly. Although the Cycle-GAN method solves the problem to some extent, the blurring degree of the fuzzy class of the paired underwater datasets generated by Cycle-GAN is relatively unitary. To solve this problem, a few-shot underwater image generative network method is proposed. Utilizing the proposed few-shot learning image generative method, the generated paired underwater datasets are better than those obtained by the Cycle-GAN method, especially under the condition of few real underwater datasets. In addition, to reconstruct high-quality results, an underwater deblurring ghost imaging method is proposed. The reconstruction method consists of two parts: reconstruction and deblurring. The experimental and simulation results show that the proposed reconstruction method has better performance in deblurring at a low sampling rate, compared with existing underwater ghost imaging methods based on deep learning. The proposed reconstruction method can effectively increase the clarity degree of the underwater reconstruction target at a low sampling rate and promotes the further applications of underwater ghost imaging. [ABSTRACT FROM AUTHOR]
- Subjects :
- *IMAGE reconstruction
*DEEP learning
*BACKSCATTERING
*PROBLEM solving
Subjects
Details
- Language :
- English
- ISSN :
- 14248220
- Volume :
- 22
- Issue :
- 16
- Database :
- Academic Search Index
- Journal :
- Sensors (14248220)
- Publication Type :
- Academic Journal
- Accession number :
- 158948335
- Full Text :
- https://doi.org/10.3390/s22166161