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A Robust Image Reflection Separation Method Based on Sift-Edge Flow

机译:一种基于筛选边缘流的鲁棒图像反射分离方法

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Motion computation is an important part for image reflection separation methods using image sequence. As the motion of background and reflection is separated on image edges, in this paper we present a robust image reflection separation method based on sift-edge flow. Edge motion is firstly computed using sift-edge flow. Based on it, the sparse motion field of image edges is clustered into two parts and refined by RANSAC, which are then used to interpolate the dense motion fields of the background and reflection. The initial image decomposition is conducted by warping the input images to reference image using the dense motion fields of background and reflection respectively. Finally, with the initial solution provided, the background and reflection images can be separated using alternating optimization. Experiment results showed that our method can achieve a robust performance compared with the state of art.
机译:运动计算是使用图像序列图像反射分离方法的重要组成部分。随着背景和反射的运动在图像边缘上分开,本文介绍了一种基于筛选边缘流的鲁棒图像反射分离方法。首先使用筛选边缘流计算边缘运动。基于它,图像边缘的稀疏运动场被聚集成两部分并由Ransac精制,然后通过RANSAC进行,然后用于内插背景和反射的密集运动场。通过分别使用背景和反射的密集运动场翘曲输入图像来进行初始图像分解。最后,利用所提供的初始解决方案,可以使用交替的优化分离背景和反射图像。实验结果表明,与现有技术相比,我们的方法可以实现强大的性能。

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