Fast Isotropic Median Filtering
This addresses a long-standing bottleneck in computational image processing by enabling more flexible and artifact-free median filtering.
The authors tackled the problem of median filtering's practical limitations regarding bit depth, kernel size, and shape, presenting a method that efficiently handles arbitrary bit-depth data, kernel sizes, and convex shapes like circles.
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.