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Computational polarization difference underwater imaging based on image fusion

机译:基于图像融合的计算偏振差水下成像

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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 ‖ and ⊥axes 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水下所获取的图像的质量,背景和遮蔽光是不偏振的还是偏极的。计算偏振差成像技术替换偏振分析仪的机械旋转,并缩短了选择最佳正交‖和⊥⊥的时间是传统PDI的改进。但是它最初通过将权重系数设置为所有像素的相同常数来获得输出图像。本文提出了一种算法来基于非附带型轮廓变换将Stokes向量的Q和U参数组合来组合STOKES向量的Q和U参数。由绿色LED阵列构建的具有偏振器的实验系统,以照亮在水中合并的平面目标和具有偏振分析器的CCD,以在不同的角度下获得目标图像来验证所提出的算法的效果。结果表明,与原始计算偏振差成像相比,我们的算法处理的输出可以显示出平面目标的更多细节,并且具有更高的对比度。

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