Underwater imaging enhancement based on a polarization filter and histogram attenuation prior

Hu, Haofeng; Qi, Pengfei; Li, Xiaobo; Cheng, Zhenzhou; Liu, Tiegen

doi:10.1088/1361-6463/abdc93

Cited by 20 publications

(18 citation statements)

References 41 publications

(72 reference statements)

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“…The other is that currently our method can only achieve CD recognition of LDOP objects and HDOP objects, but cannot further distinguish objects with small DOP differences (e.g., the metal game coin (p optimum targ = 0.98) and metal sheet (p optimum targ = 1) that both belong to the HDOP object in case 3). In order to improve the adaptability of our methods for the aforementioned problems, operations such as dark channel prior [21] or histogram optimization [22][23][24] could be used to improve object CD recognition in high-turbidity or low-light conditions. The sensitivity of object CD recognition can be increased with the help of computer vision [25].…”

Section: Discussionmentioning

confidence: 99%

Local optimum underwater polarization imaging enhancement based on connected domain prior

Zhang¹,

Zhou²,

Meng³

et al. 2022

J. Opt.

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Estimating the degree of polarization (DOP) of target signal light (ptarg) is a critical step in underwater polarization differential imaging process. It is challenging to accurately estimate the ptarg of objects containing multiple attributes in the imaging range. Inspired by the optical polarization characteristics of objects with different DOP, we find a prior of connected domains (CDs) of objects in polarization orthogonal images. Based on this prior, the CDs of LDOP and HDOP objects are extracted from the darkest image (Imin) and brightest image (Imax) without additional image acquisition. The ptarg of LDOP and HDOP objects in the scene can be estimated through the extracted CDs, which realizes the transition from global optimum estimation in the traditional method to local optimum estimation. According to the estimated ptarg, the enhanced results of the entire image marked by ptarg can be obtained after fusing the enhanced results of the objects in each CD. Experimental results of simple and complex combination scenes of objects show that our method can recognize CDs of LDOP and HDOP objects. We obtained the enhanced results of the entire image, verified the feasibility and robustness of our method, and the CDs of LDOP and HDOP objects could be effectively extracted in up to 34.3 NTU of turbid water. The local optimum improves the application ability of underwater polarization imaging in unpredictable scenarios, while the enhanced result of target labeling has potential application value in underwater target recognition and classification.

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Section: Discussionmentioning

confidence: 99%

Local optimum underwater polarization imaging enhancement based on connected domain prior

Zhang¹,

Zhou²,

Meng³

et al. 2022

J. Opt.

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“…Liang (Liang et al, 2015) et al developed Schechner's basic model and proposed a new polarization angle (AoP)-based polarimetric method to handle the image degeneration in scattering media, including foggy, hazy, and turbid environments. In 2021, Qi (Hu et al, 2021) designed a particular polarimetric method based on the typical polarization difference model and addressed the underwater color imaging issue well. This method solves the problem of HS for processing colors, ensuring the accuracy of mutually orthogonal polarization information recovery.…”

Section: Introductionmentioning

confidence: 99%

UCRNet: Underwater color image restoration via a polarization-guided convolutional neural network

Huang

et al. 2022

Front. Mar. Sci.

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Underwater images always suffer from low contrast and color distortion due to the wavelength-dependent scattering and absorption effects caused by particles existing in turbid water, especially in high turbidity conditions. Based on the polarization properties of the backscattering light, polarimetric methods can estimate the intensity level of the backscattering and the transmittance of the media. Accordingly, they can separate the target signal from the undesired ones to achieve high-quality imaging. In addition, learning-based polarimetric methods are effective for gray-model image restoration, but the learning-based polarimetric technique for color image restoration has yet to be considered. In this paper, we propose a 3- dimensional convolutional neural network, which maintains the correlation of polarization information among different polarization channel images as well as embodies polarization constraints, for underwater color image restoration. The experimental results verify that the proposed solution improves the image quality (i.e., the image contrast, details, and color) and outperforms other existing methods, especially when the turbidity of scattering media is high. The proposed solution can be readily applied to practical applications and potentially realize the clear vision in other scattering media, including biomedical imaging and remote sensing.

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“…There are significant differences between underwater imaging and imaging in air, because underwater imaging is affected by two distinct properties of water. One is the absorption and scattering effect of water, 8 the other is the refraction effect of water 9 . When light passes through the water into the camera, the image captured by the camera becomes blurred and its color changes 10 .…”

Section: Introductionmentioning

confidence: 99%

Forward projection multilayer refraction model for underwater camera calibration

Wang

Fan

et al. 2022

Opt. Eng.

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Unlike imaging in air, underwater imaging is affected by two distinct properties of water: the absorption and scattering effect of water and the refraction effect of water, resulting in the conventional pinhole model no longer being suitable for underwater imaging. Therefore, without prior calibration in air to calculate the camera intrinsic parameters and distortions for subsequent underwater calibration, an underwater multilayer refraction model based on forward projection method is represented. Along the direction of light propagation, the multilayer refraction model presents a unified scale factor instead of focal length during the perspective projection transformation from the camera coordinate system to the image coordinate system. Simulations regarding the analysis of different medium thicknesses, different medium refractive indices, and different point pixels are performed to explore the influence of the above three parameters on the refraction model. Through the underwater parameter identification and verification, the proposed multilayer refraction model has high measurement accuracy, excellent solution efficiency, stable identification results, more identification parameters, and wide application fields, which are suitable for both single-time refraction and double-time refraction.

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Underwater imaging enhancement based on a polarization filter and histogram attenuation prior

Cited by 20 publications

References 41 publications

Local optimum underwater polarization imaging enhancement based on connected domain prior

Local optimum underwater polarization imaging enhancement based on connected domain prior

UCRNet: Underwater color image restoration via a polarization-guided convolutional neural network

Forward projection multilayer refraction model for underwater camera calibration

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