Anisotropic Joint Trilateral Rolling Filter for Image Smoothing

Xiaohua Zhang, Yuelan Xin, Ning Xie


Filtering an image by eliminating noise and irrelevant details while preserving prominent structure edges is an important pre-processing task in many fields, such as image processing and computer vision. In this study, a novel approach called anisotropic joint trilateral rolling filter (AJTRF) is proposed for the smoothing of an image while preserving important structure edges. AJTRF works in an iterative manner and extends the joint bilateral filter to a joint trilateral filter by employing additional weights in eigen space. During filtering, the range weights are recalculated in each iteration, while the spatial Gaussian weights are computed in anisotropic orientations instead of isotropic orientations. Furthermore, the inner products between anisotropic orientations (expressed as eigenvectors) are used as weights to measure orientation similarities of pixels. Structure tensors at each pixel are calculated to determine the structure orientations, which are used as the anisotropic orientations. A range of experiments are conducted. The results demonstrate that the proposed AJTRF has stronger smoothing and structure preserving ability than established methods.

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