Cross-based local multipoint filtering

Abstract

This paper presents a cross-based framework of performing local multipoint filtering efficiently. We formulate the filtering process as a local multipoint regression problem, consisting of two main steps: 1) multipoint estimation, calculating the estimates for a set of points within a shape-adaptive local support, and 2) aggregation, fusing a number of multipoint estimates available for each point. Compared with the guided filter that applies the linear regression to all pixels covered by a fixed-sized square window non-adaptively, the proposed filtering framework is a more generalized form. Two specific filtering methods are instantiated from this framework, based on piecewise constant and piecewise linear modeling, respectively. Leveraging a cross-based local support representation and integration technique, the proposed filtering methods achieve theoretically strong results in an efficient manner, with the two main steps' complexity independent of the filtering kernel size. We demonstrate the strength of the proposed filters in various applications including stereo matching, depth map enhancement, edge-preserving smoothing, color image denoising, detail enhancement, and flash/no-flash denoising.