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An Extension Of Min/max Flow Framework

机译:最小/最大流量框架的扩展

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In this paper, the min/max flow scheme for image restoration is revised. The novelty consists of the following three parts. The first is to analyze the reason of the speckle generation and then to modify the original scheme. The second is to point out that the continued application of this scheme cannot result in an adaptive stopping of the curvature flow. This is followed by modifications of the original scheme through the introduction of the Gradient Vector Flow (GVF) field and the zero-crossing detector, so as to control the smoothing effect. Our experimental results with image restoration show that the proposed schemes can reach a steady state solution while preserving the essential structures of objects. The third is to extend the min/max flow scheme to deal with the boundary leaking problem, which is indeed an intrinsic shortcoming of the familiar geodesic active contour model. The min/max flow framework provides us with an effective way to approximate the optimal solution. From an implementation point of view, this extended scheme makes the speed function simpler and more flexible. The experimental results of segmentation and region tracking show that the boundary leaking problem can be effectively suppressed.
机译:本文修改了图像恢复的最小/最大流方案。新颖性包括以下三个部分。首先是分析产生斑点的原因,然后修改原始方案。第二点是要指出的是,该方案的继续应用不会导致曲率流的自适应停止。接下来,通过引入梯度矢量流(GVF)字段和过零检测器来对原始方案进行修改,以控制平滑效果。我们的图像恢复实验结果表明,所提出的方案可以在保持物体基本结构的同时达到稳态解。第三是扩展最小/最大流量方案以处理边界泄漏问题,这确实是熟悉的测地线活动轮廓模型的固有缺点。最小/最大流量框架为我们提供了一种逼近最佳解决方案的有效方法。从实现的角度来看,这种扩展方案使速度功能更简单,更灵活。分割和区域跟踪的实验结果表明,可以有效地抑制边界泄漏问题。

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