A neural network for underwater polarization dehazing imaging

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

doi:10.1117/12.2602463

Cited by 4 publications

(4 citation statements)

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“…Underwater optical imaging suffers from reduced image clarity due to the scattering and absorption of the water medium and underwater suspended particles. 1,2 Underwater polarization imaging uses the polarization characteristic difference between the reflected light and the background scattering light to separate them, which has important practical value and broad development prospect. 3 In recent years, researchers at home and abroad have conducted extensive research on underwater polarization imaging technology and achieved certain results.…”

Section: Introductionmentioning

confidence: 99%

Research on improving the clarity of underwater polarization imaging image restoration

Liu,

Luo,

2024

J. Electron. Imag.

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To solve the problem of low resolution of underwater polarization imaging images, a method to improve the resolution of active underwater polarization imaging images is proposed, which includes an optimal orthogonal polarization image group acquisition method, and an accurate estimation and optimization method of the polarization degree of backscattered light and target reflected light. An experiment is carried out to improve the resolution of underwater image restoration based on linear polarized light active illumination. The experimental results show that the image group with the lowest correlation degree is used as the orthogonal polarization image group among multiple groups of orthogonal polarization images, and the scattering depolarization coefficient is used to accurately estimate and optimize the polarization degree of backscattered light and target reflected light, and the final restored target image has higher resolution. Compared with the existing image restoration algorithms, it has obvious advantages and is suitable for water media with different material objects and different turbidity.

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Section: Introductionmentioning

confidence: 99%

Research on improving the clarity of underwater polarization imaging image restoration

Liu,

Luo,

2024

J. Electron. Imag.

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“…Nevertheless, in the challenging underwater environment, the obtained image still suffers from different degrees of degradation, such as color distortion [6][7][8][9][10], low light and contrast [11,12], and haze-like effects [11][12][13][14][15][16][17][18][19]. The original YOLOv5 model is easily affected by the above problems.…”

Section: Introductionmentioning

confidence: 99%

An Improved YOLOv5 Underwater Detector Based on an Attention Mechanism and Multi-Branch Reparameterization Module

et al. 2023

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Underwater target detection is a critical task in various applications, including environmental monitoring, underwater exploration, and marine resource management. As the demand for underwater observation and exploitation continues to grow, there is a greater need for reliable and efficient methods of detecting underwater targets. However, the unique underwater environment often leads to significant degradation of the image quality, which results in reduced detection accuracy. This paper proposes an improved YOLOv5 underwater-target-detection network to enhance accuracy and reduce missed detection. First, we added the global attention mechanism (GAM) to the backbone network, which could retain the channel and spatial information to a greater extent and strengthen cross-dimensional interaction so as to improve the ability of the backbone network to extract features. Then, we introduced the fusion block based on DAMO-YOLO for the neck, which enhanced the system’s ability to extract features at different scales. Finally, we used the SIoU loss to measure the degree of matching between the target box and the regression box, which accelerated the convergence and improved the accuracy. The results obtained from experiments on the URPC2019 dataset revealed that our model achieved an mAP@0.5 score of 80.2%, representing a 1.8% and 2.3% increase in performance compared to YOLOv7 and YOLOv8, respectively, which means our method achieved state-of-the-art (SOTA) performance. Moreover, additional evaluations on the MS COCO dataset indicated that our model’s mAP@0.5:0.95 reached 51.0%, surpassing advanced methods such as ViDT and RF-Next, demonstrating the versatility of our enhanced model architecture.

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“…Underwater imaging technologies have attracted extensive attention and research 1 – 3 due to their wide range of applications in underwater rescue, marine resource exploration, and other fields. However, the improvement of the efficiency and quality of underwater imaging remains a challenging topic because of the existence of scattering and absorption of the target light by suspended particles in the water.…”

Section: Introductionmentioning

confidence: 99%

“…A large number of underwater dehazing imaging methods have been proposed to improve the quality of underwater imaging 1 – 7 In particular, the polarization-based underwater dehazing imaging technology has received increased attention after the classic underwater polarization dehazing imaging model proposed by Schechner and co-authors 3 , 4 . Many methods have been proposed to improve the performance of underwater polarimetric dehazing imaging based on Schechner’s proposal using the relationship between the object radiation and two orthogonal polarization images 3 .…”

Section: Introductionmentioning

confidence: 99%

Fast processing of underwater active polarimetric dehazing imaging without prior knowledge

Xiang,

Wang,

Gao

et al. 2023

J. Electron. Imag.

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.The scattering and absorption of suspended particles in water severely restrict the quality of underwater imaging. Several works have been proposed to improve the dehazing effect, but their efficiency was not considered. However, the processing efficiency of dehazing is an important indicator of the computing cost in an active application. An improved dehazing method that analyzes and optimizes the estimation accuracy of polarization parameters in the traditional underwater polarization imaging model is proposed. The degree of linear polarization of the target object light and the backscattered light and the transmission coefficient in the scattering medium are jointly estimated by the stochastic gradient descent method. The experimental results indicate that the proposed method can better preserve image details at a faster speed than the traditional underwater dehazing imaging method, especially in high-turbidity water environments. The average speed of the algorithm for restoring a 1024 × 768 pixel image is about 6.2 ms.

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A neural network for underwater polarization dehazing imaging

Cited by 4 publications

References 0 publications

Research on improving the clarity of underwater polarization imaging image restoration

Research on improving the clarity of underwater polarization imaging image restoration

An Improved YOLOv5 Underwater Detector Based on an Attention Mechanism and Multi-Branch Reparameterization Module

Fast processing of underwater active polarimetric dehazing imaging without prior knowledge

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