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A Computing Framework of Adaptive Support-Window Multi-Lateral Filter for Image and Depth Processing
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DOI：10.1109/TBC.2014.2330391.png)
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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 similar to 49X and 1.3X similar to 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.
Keyword:
Bilateral filter
adaptive support window
data aggregation
integral image
depth image processing
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