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Recovering intrinsic images from image sequences using total variation models

机译:使用总变异模型从图像序列中恢复固有图像

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Recovering intrinsic images from natural photos is one of the foundational problems in computer vision. This mission always falls into an ill-posed problem. In order to attain reasonable estimations, one strategy is to use multiple images of the scene under various lightings so as to narrow the solution space, whereas another is to utilize priori knowledge as constraints. In this paper, we present an approach to deriving intrinsic images (including illumination images and reflectance images) that employs both strategies. Specifically, the Total Variation (TV) constraint is imposed because of its excellent edge preservation ability and simple parameter settings. To solve this objective function efficiently, we propose using the Alternating Direction Method of Multipliers (AD-MM) to build an iterative numerical scheme. Experimental results illustrate the effectiveness of the proposed model and the numerical scheme.
机译:从自然照片中恢复内在图像是计算机视觉的基本问题之一。这个任务总是陷入一个不适的问题。为了获得合理的估计,一种策略是在各种照明条件下使用场景的多个图像,以缩小求解空间,而另一种策略是利用先验知识作为约束。在本文中,我们提出了一种采用两种策略来推导固有图像(包括照明图像和反射图像)的方法。具体来说,由于其出色的边缘保留能力和简单的参数设置,因此施加了总变化(TV)约束。为了有效地解决该目标函数,我们建议使用交替方向乘数法(AD-MM)来构建迭代数值方案。实验结果说明了所提模型和数值方案的有效性。

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