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Three-dimensional Integral Imaging Visualization in Scattering Medium with Baysian Estimation

机译:贝叶斯估计的散射介质三维积分成像可视化

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We propose an image descattering method for improved image visualization in scattering medium such as heavy turbid water. The descattering method is applied to three-dimensional (3D) integral imaging (InIm). The 3D InIm pick-up process captures multiple two-dimensional images of a scene each having a different perspective of the scene, known as elemental images (EIs), and combines this information to generate a 3D reconstruction of the scene. A scattering mitigation process is applied to each EI prior to 3D reconstruction to reduce the effect of scattering. In the scattering mitigation process, we assume that the intensity of the scattering medium containing object information is a Gaussian distribution and the distribution of the scattering medium is known a priori and also follows a Gaussian distribution for the whole captured image. By computing maximum a posteriori estimates of the mean and variance of the turbid media containing object information, a Bayesian scattering mitigation process is implemented. After the scattering mitigation process, the 3D reconstruction image is calculated computationally using back propagation method. We compare the reconstructed image quality of an existing method and our proposed method using structural similarity index (SSIM), and show our proposed method achieve higher SSIM.
机译:我们提出一种图像去散射方法,以改善散射介质(如重浑水)中的图像可视化。消散方法应用于三维(3D)积分成像(InIm)。 3D InIm拾取过程捕获场景的多个二维图像(每个元素具有不同的场景视角),称为元素图像(EI),并将这些信息组合起来以生成场景的3D重建。在3D重建之前将散射缓解过程应用于每个EI,以减少散射的影响。在散射缓解过程中,我们假设包含对象信息的散射介质的强度是高斯分布,并且散射介质的分布是先验已知的,并且对于整个捕获图像也遵循高斯分布。通过计算包含对象信息的混浊介质的均值和方差的最大后验估计,可以实现贝叶斯散射缓解过程。在散射缓解过程之​​后,使用反向传播方法通过计算来计算3D重建图像。我们比较了现有方法和我们提出的方法使用结构相似性指数(SSIM)的重建图像质量,并表明我们提出的方法实现了更高的SSIM。

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