{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T22:44:54Z","timestamp":1768344294297,"version":"3.49.0"},"reference-count":41,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2022,8,17]],"date-time":"2022-08-17T00:00:00Z","timestamp":1660694400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s22166161","type":"journal-article","created":{"date-parts":[[2022,8,17]],"date-time":"2022-08-17T22:53:30Z","timestamp":1660776810000},"page":"6161","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Deblurring Ghost Imaging Reconstruction Based on Underwater Dataset Generated by Few-Shot Learning"],"prefix":"10.3390","volume":"22","author":[{"given":"Xu","family":"Yang","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China"}]},{"given":"Zhongyang","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China"}]},{"given":"Pengfei","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China"}]},{"given":"Lu","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China"}]},{"given":"Jiemin","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China"}]},{"given":"Long","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China"}]},{"given":"Bo","family":"Zou","sequence":"additional","affiliation":[{"name":"Institute of Land Aviation, Beijing 101121, China"}]},{"given":"Yong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Optical Target Simulation and Test Technology, Harbin Institute of Technology, Harbin 150001, China"}]},{"given":"Jianlong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Optical Target Simulation and Test Technology, Harbin Institute of Technology, Harbin 150001, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1126\/science.1260088","article-title":"Expansion microscopy","volume":"347","author":"Chen","year":"2015","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1364\/OE.27.000621","article-title":"Enhancing underwater optical imaging by using a low-pass polarization fifilter","volume":"27","author":"Amer","year":"2019","journal-title":"Opt. 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