Fast Guided Median Filter
Fast Guided Median Filter
复制标题
DOI：
10.1109/tip.2022.3232916
复制
发表时间：
2023-01-01
影响因子：
10.6
通讯作者：
Mishiba, Kazu
中科院分区：
文献类型：
--
作者：
Mishiba, Kazu
加权中值（WM）滤波器的快速计算受到对每个局部数据窗口构建加权直方图的阻碍。由于每个局部窗口计算的权重不同，使用滑动窗口方法很难有效地构建加权直方图。本文提出了一种克服直方图构造困难的新型WM滤波器。该方法实现了对高分辨率图像的实时处理，适用于多维、多通道、高精度的数据处理。我们的WM滤波器中使用的权核是点导滤波器，它是由导滤波器衍生而来的。基于引导滤波器的核的使用避免了梯度反转伪影，并且显示出比基于颜色/强度距离的高斯核更高的去噪性能。该方法的核心思想是允许使用直方图更新和滑动窗口方法来找到加权中位数的公式。对于高精度数据，我们提出了一种基于链表的算法，该算法可以减少直方图存储的内存需求和更新直方图的计算成本。我们提出了适用于CPU和GPU的实现方法。实验结果表明，该方法确实实现了比传统WM滤波器更快的计算速度，并且能够过滤多维、多通道和高精度的数据。这是一种传统方法难以实现的方法。
Faster computation of a weighted median (WM) filter is impeded by the construction of a weighted histogram for every local window of data. Since the calculated weights vary for each local window, it is difficult, using a sliding window approach, to construct the weighted histogram efficiently. In this paper, we propose a novel WM filter that overcomes the difficulty of histogram construction. Our proposed method achieves real-time processing for higher resolution images and can be applied to multidimensional, multichannel, and high precision data. The weight kernel used in our WM filter is the pointwise guided filter, which is derived from the guided filter. The use of kernels based on the guided filter avoids gradient reversal artifacts and shows a higher denoising performance than the Gaussian kernel based on the color/intensity distance. The core idea of the proposed method is a formulation that allows the use of histogram updates with a sliding window approach to find the weighted median. For high precision data we propose an algorithm based on a linked list that can reduce the memory requirements of storing histograms and the computational cost of updating them. We present implementations of the proposed method that are suitable for both CPU and GPU. Experimental results show that the proposed method indeed realizes faster computation than conventional WM filters and is capable of filtering multidimensional, multichannel, and high precision data. This is an approach which is difficult to achieve with conventional methods.