A Computing Framework of Adaptive Support-Window Multi-Lateral Filter for Image and Depth Processing

In this paper, a computing framework for adaptive-support-window-based multi-lateral filters is proposed. The so-called multi-lateral filters, extended from the well-known bilateral filter, have found their broaden applications in noise/high-frequency suppression for 2-D image and 3-D depth processing. Our filters rely on binarizing the traditional pixel-wise weights to be only 0 or 1, resulting in an adaptive support window whose shape depends on the local image structure of the central anchor pixel. A cross-subwindow-based algorithm is devised to compute the adaptive support window. A fast algorithm based on integral images is also devised for data aggregation within such an irregularly shaped support window. Taking advantage of the integral images, our scheme presents a near constant-time complexity regardless of the size and shape of the support window. Experiments show that both noise suppression and edge-preserving can be simultaneously achieved by using our proposed framework. The average speedup ratios of our scheme are 14X~49X and 1.3X~5.6X against the traditional and the O(1) implementations, respectively. Our scheme also has the advantage of easy extension to tri-lateral and quadri-lateral filters, whereas other O(1) algorithms might not.