The underwater active imaging detection with linear polarization

Ren, Qiming; Xiang, Yanfa; Chen, Rui-pin

doi:10.1117/12.2573664

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…5. In addition, compared to the image recovery results by using different dehazing methods: the polarization dehazing [29] (see plots (II) in Fig. 5), the CNN polarization dehazing [21] (see plots (III) in Fig.…”

Section: Resultsmentioning

confidence: 99%

Underwater Polarization Imaging Recovery Based on Polarimetric Residual Dense Network

et al. 2022

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Application of deep-learning to polarization imaging technology for image restoration has led to many technological breakthroughs, especially in underwater image recovery and recognition. In this work, a four-input deep learning model with the Polarimetric Residual Dense Network is proposed for underwater image recovery. The diverse polarization component images are trained and tested in different processes in the network for the recognition and dehazing by considering the physical model of polarization dehazing imaging. Our study reveals that the proposed method can efficiently recover the hazed images, and provide good performance for improving the quality of image restoration even in a high-turbidity complex underwater environment.

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Section: Resultsmentioning

confidence: 99%

Underwater Polarization Imaging Recovery Based on Polarimetric Residual Dense Network

et al. 2022

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“…Through theoretical analysis, Ren conducted polarization imaging experiments on water bodies with different turbidity and different materials. The mechanism of active linear polarization imaging was demonstrated [16]. Wang proposed an underwater polarization imaging method based on a Mueller imager.…”

Section: Introductionmentioning

confidence: 99%

Polarization Imaging Method for Underwater Low-Visibility Metal Target Using Focus Dividing Plane

Xue

Zhao

et al. 2023

Applied Sciences

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Aiming at the problems of brightness attenuation and contrast reduction in the target image caused by underwater low-visibility environments, a metal target detection method based on split-focus plane polarization imaging is proposed. Firstly, a hybrid enhancement method is proposed to clarify the degraded polarization image. In this study, the GrayWorld method is improved to compensate the attenuation difference of the total light intensity of the polarization image. Variational contrast and saturation enhancement algorithms are used to reduce the underwater scattering effect; secondly, a split-focus plane polarization imaging system is built to complete the control of camera parameters, polarization image acquisition and information processing. Under different underwater low-visibility conditions, polarization imaging of targets with different materials can be realized; finally, an image quality evaluation system is constructed to compare the light intensity and degree of polarization images that are collected by the focal plane polarization imaging system. The polarization characteristics of metal and nonmetal target plates are analyzed. The results show that under the condition of low visibility, the obtained polarization image contrast of the metal target is relatively high, and its EME, information entropy and average gradient are increased by 183.82%, 53.46% and 586.22% on average relative to the image of light intensity. In an underwater low-visibility scene, the method of focal plane polarization imaging proposed in this paper can reduce the difficulty of metal target detection.

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The underwater active imaging detection with linear polarization

Cited by 2 publications

References 9 publications

Underwater Polarization Imaging Recovery Based on Polarimetric Residual Dense Network

Underwater Polarization Imaging Recovery Based on Polarimetric Residual Dense Network

Polarization Imaging Method for Underwater Low-Visibility Metal Target Using Focus Dividing Plane

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