Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81319
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dc.contributor.authorZhou, LNen_US
dc.contributor.authorXiao, Yen_US
dc.contributor.authorChen, Wen_US
dc.date.accessioned2019-09-20T00:55:02Z-
dc.date.available2019-09-20T00:55:02Z-
dc.date.issued2019-
dc.identifier.citationIEEE photonics journal, Aug. 2019, v. 11, no. 4, 7801315, p. 1-16en_US
dc.identifier.urihttp://hdl.handle.net/10397/81319-
dc.description.abstractUnderwater imaging has been extensively studied to bypass the limitation aroused by scattering and absorption of water solutions. It is highly meaningful to the development of optical imaging, especially in turbid media. The existing methods developed for reconstruction of original images from speckle patterns are applied in a stable medium, which obstruct wider applications in unpredictable media. Hence, it is crucial to take changeable environments into consideration to circumvent the limits of the extant methods. In this paper, we propose a new approach based on cosine similarity for speckle classification and convolutional neural network (CNN) for the reconstruction. The targets are placed in variant densities of turbid water mixed with certain of milk, and their corresponding intensity speckle patterns are recorded by a camera. It is verified that utilization of cosine similarity for the classification of patterns recorded in changeable media ensures high fidelity for label predictions. For a speckle pattern obtained in totally unidentified media, it can make a prediction of the density which has a high probability of accuracy. We can exploit the classified density to automatically select the most appropriate datasets to train a CNN model and then make predictions in real time with the trained CNN model. The combined model presented in this paper is tolerant to the uncertainty of turbidity. Moreover, it guarantees high-accuracy pattern classification and high-quality image reconstruction. It is feasible for potential applications in harsh water solutions with unknown perturbations of concentrations.en_US
dc.description.sponsorshipChinese Mainland Affairs Officeen_US
dc.description.sponsorshipDepartment of Electronic and Information Engineeringen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.relation.ispartofIEEE photonics journalen_US
dc.rightsThis work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/en_US
dc.rightsThe following publication L. Zhou, Y. Xiao and W. Chen, "Imaging Through Turbid Media With Vague Concentrations Based on Cosine Similarity and Convolutional Neural Network," in IEEE Photonics Journal, vol. 11, no. 4, pp. 1-15, Aug. 2019, Art no. 7801315 is available at https://dx.doi.org/10.1109/JPHOT.2019.2927746en_US
dc.subjectImage reconstructionen_US
dc.subjectUnderwater imagingen_US
dc.subjectTurbid mediaen_US
dc.subjectCosine similarityen_US
dc.subjectDeep learningen_US
dc.titleImaging through turbid media with vague concentrations based on cosine similarity and convolutional neural networken_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage1en_US
dc.identifier.epage16en_US
dc.identifier.volume11en_US
dc.identifier.issue4en_US
dc.identifier.doi10.1109/JPHOT.2019.2927746en_US
dc.identifier.isiWOS:000478658500001-
dc.identifier.scopus2-s2.0-85073886841-
dc.identifier.eissn1943-0655en_US
dc.identifier.artn7801315en_US
dc.description.validate201909 bcrc-
dc.description.oapublished_final-
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