Fast Scale-Aware Image Filtering via Half-Box Regions

Abstract

Digital image has become an important data representation in precision engineering due to recent advances in image acquisition and machine learning technologies. The contents of a digital image consists of various scales, and therefore scale-aware image filter, which removes small structures while preserving salient features, is useful in many applications, but is computationally expensive in practice. This paper proposes a simple and fast computational method for scale-aware image filtering based on half-box regions. The proposed method consists of a recursive process of joint bilateral color averaging approximated by assuming constant bilateral weights within each half-box region adjacent to a given pixel. These bilateral weights are designed to also remain constant during the recursion process. Therefore, they only need to be calculated once, which reduces the computational cost dramatically. The color sums within the half-box regions are efficiently calculated by applying only one fast box filter using the relative coordinate relationship between the pixel and each half-box center. The quality, speed, and convergence rate of the proposed method were also examined by numerically comparing it with conventional methods, and it was fond that the proposed method achieved high performance.