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Computational polarization difference underwater imaging based on image fusion

机译:基于图像融合的计算极化差水下成像

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Polarization difference imaging can improve the quality of images acquired underwater, whether the background and veiling light are unpolarized or partial polarized. Computational polarization difference imaging technique which replaces the mechanical rotation of polarization analyzer and shortens the time spent to select the optimum orthogonal 1 and Laxes is the improvement of the conventional PDI. But it originally gets the output image by setting the weight coefficient manually to an identical constant for all pixels. In this paper, a kind of algorithm is proposed to combine the Q and U parameters of the Stokes vector through pixel-level image fusion theory based on non-subsample contourlet transform. The experimental system built by the green LED array with polarizer to illuminate a piece of flat target merged in water and the CCD with polarization analyzer to obtain target image under different angle is used to verify the effect of the proposed algorithm. The results showed that the output processed by our algorithm could show more details of the flat target and had higher contrast compared to original computational polarization difference imaging.
机译:偏振差成像可以提高在水下获取的图像的质量,无论背景光和遮罩光是非偏振还是部分偏振。取代偏振分析仪的机械旋转并缩短选择最佳正交1和Laxes所需时间的计算偏振差成像技术是对传统PDI的改进。但是它最初是通过手动将权重系数设置为所有像素的相同常数来获取输出图像的。本文提出了一种基于非子样本Contourlet变换的像素级图像融合理论,将Stokes向量的Q和U参数组合在一起的算法。以带偏光器的绿色LED阵列照明并融合在水中的扁平目标为实验对象,并利用偏光分析仪建立的CCD以不同角度获取目标图像的实验系统,验证了该算法的有效性。结果表明,与原始的计算偏振差成像相比,我们的算法处理的输出可以显示更多平坦目标的细节,并且具有更高的对比度。

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