Visual Computing Group, Microsoft Research Asia, Microsoft Building 2, #5 Dan Leng Street, Hai Dian District, Beijing 100080, China.
IEEE Trans Pattern Anal Mach Intell. 2013 Jun;35(6):1397-409. doi: 10.1109/TPAMI.2012.213.
In this paper, we propose a novel explicit image filter called guided filter. Derived from a local linear model, the guided filter computes the filtering output by considering the content of a guidance image, which can be the input image itself or another different image. The guided filter can be used as an edge-preserving smoothing operator like the popular bilateral filter [1], but it has better behaviors near edges. The guided filter is also a more generic concept beyond smoothing: It can transfer the structures of the guidance image to the filtering output, enabling new filtering applications like dehazing and guided feathering. Moreover, the guided filter naturally has a fast and nonapproximate linear time algorithm, regardless of the kernel size and the intensity range. Currently, it is one of the fastest edge-preserving filters. Experiments show that the guided filter is both effective and efficient in a great variety of computer vision and computer graphics applications, including edge-aware smoothing, detail enhancement, HDR compression, image matting/feathering, dehazing, joint upsampling, etc.
在本文中，我们提出了一种新的显式图像滤波器，称为导向滤波器。该滤波器源自局部线性模型，通过考虑导向图像的内容（可以是输入图像本身或另一幅不同的图像）来计算滤波输出。导向滤波器可用作边缘保持平滑算子，类似于流行的双边滤波器[1]，但在边缘附近具有更好的性能。导向滤波器也是一种比平滑更通用的概念：它可以将导向图像的结构传递到滤波输出，从而实现新的滤波应用，如去雾和导向羽化。此外，导向滤波器自然具有快速且非近似的线性时间算法，无论核大小和强度范围如何。目前，它是最快的边缘保持滤波器之一。实验表明，导向滤波器在各种计算机视觉和计算机图形学应用中都非常有效和高效，包括边缘感知平滑、细节增强、HDR 压缩、图像抠图/羽化、去雾、联合上采样等。
