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

Abstract

In this paper, a computing framework for adaptivesupport-window-based multi-lateral filters is proposed. The socalled multi-lateral filters, extended from the well-known bilateral filter, have found their broaden applications in noise/highfrequency 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 constanttime 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.Index Terms-Bilateral filter, adaptive support window, data aggregation, integral image, depth image processing

Video quality enhancement based on visual attention model and multi-level exposure correction

“…Based on the concept of [22], we first smooth each intermediate image by using the bilateral filter [16] to extract the detail information from intermediate resulting images. Then the difference ω q D between the intermediate image and its smoothed version is computed as follows:…”

“…As we know, the bilateral filtering remains the main structure and removes the details in an image [16]. Therefore, Eq.…”

Decomposed Multilateral Filtering for Accelerating Filtering with Multiple Guidance Images

“…There are two categories for using multiple guidance images in image filtering: high-dimensional filtering [ 30 , 33 , 34 , 35 , 36 , 37 ] and multilateral filtering [ 29 , 31 , 32 , 38 , 39 ]. The former is additive logic, and the latter is multiplicative logic for additional kernels.…”