提出首个能处理任意尺寸、形状和位深的快速中值滤波方法。
Fast Isotropic Median Filtering
- 基于新算法实现任意凸形核的高效中值滤波
- 支持任意位深图像，且无交叉条纹伪影
- 适合需要高保真边缘保留的图像处理场景
中值滤波是计算图像处理的核心技术，能在最小模糊和边缘软化下有效平滑图像，对伽马调整等单调变换不变，且对噪声和异常值具有鲁棒性。然而，现有算法普遍存在实际限制：图像数据的位深度、滤波核尺寸或核形状本身。方形核实现常产生条纹状交叉伪影，且几乎所有高效算法在实践中仅限于方形核。本文首次提出一种克服所有这些限制的方法，可在任意位深数据、任意核大小和任意凸形核（包括圆形）上高效运行。
原文摘要 · Abstract (English)
Median filtering is a cornerstone of computational image processing. It provides an effective means of image smoothing, with minimal blurring or softening of edges, invariance to monotonic transformations such as gamma adjustment, and robustness to noise and outliers. However, known algorithms have all suffered from practical limitations: the bit depth of the image data, the size of the filter kernel, or the kernel shape itself. Square-kernel implementations tend to produce streaky cross-hatching artifacts, and nearly all known efficient algorithms are in practice limited to square kernels. We present for the first time a method that overcomes all of these limitations. Our method operates efficiently on arbitrary bit-depth data, arbitrary kernel sizes, and arbitrary convex kernel shapes, including circular shapes.
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