Nogami Haruki, Kanetaka Yamato, Naganawa Yuki, Maeda Yoshihiro, Fukushima Norishige
Department of Computer Science, Faculty of Engineering, Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya 466-8555, Japan.
Department of Electrical Engineering, Faculty of Engineering, Tokyo University of Science, Tokyo 125-8585, Japan.
Sensors (Basel). 2024 Jan 19;24(2):633. doi: 10.3390/s24020633.
This paper proposes an efficient algorithm for edge-preserving filtering with multiple guidance images, so-called multilateral filtering. Multimodal signal processing for sensor fusion is increasingly important in image sensing. Edge-preserving filtering is available for various sensor fusion applications, such as estimating scene properties and refining inverse-rendered images. The main application is joint edge-preserving filtering, which can preferably reflect the edge information of a guidance image from an additional sensor. The drawback of edge-preserving filtering lies in its long computational time; thus, many acceleration methods have been proposed. However, most accelerated filtering cannot handle multiple guidance information well, although the multiple guidance information provides us with various benefits. Therefore, we extend the efficient edge-preserving filters so that they can use additional multiple guidance images. Our algorithm, named decomposes multilateral filtering (DMF), can extend the efficient filtering methods to the multilateral filtering method, which decomposes the filter into a set of constant-time filtering. Experimental results show that our algorithm performs efficiently and is sufficient for various applications.
本文提出了一种用于多引导图像保边滤波的高效算法，即多边滤波。传感器融合的多模态信号处理在图像传感中变得越来越重要。保边滤波可用于各种传感器融合应用，如估计场景属性和细化逆渲染图像。主要应用是联合保边滤波，它可以更好地反映来自附加传感器的引导图像的边缘信息。保边滤波的缺点在于其计算时间长；因此，人们提出了许多加速方法。然而，大多数加速滤波不能很好地处理多引导信息，尽管多引导信息为我们带来了各种好处。因此，我们扩展了高效的保边滤波器，使其能够使用额外的多引导图像。我们的算法称为分解多边滤波（DMF），它可以将高效滤波方法扩展到多边滤波方法，即将滤波器分解为一组固定时间的滤波。实验结果表明，我们的算法执行效率高，足以满足各种应用。
