SPATIAL-VARIANT MORPHOLOGICAL FILTERS WITH NONLOCAL-PATCH-DISTANCE-BASED AMOEBA KERNEL FOR IMAGE DENOISING

Abstract

Filters of the Spatial-Variant amoeba morphology can preserve edges better, but with too much noise being left. For better denoising, this paper presents a new method to generate structuring elements for Spatially-Variant amoeba morphology.  The amoeba kernel in the proposed strategy is divided into two parts: one is the patch distance based amoeba center, and another is the geodesic distance based amoeba boundary, by which the nonlocal patch distance and local geodesic distance are both taken into consideration. Compared to traditional amoeba kernel, the new one has more stable center and its shape can be less influenced by noise in pilot image. What’s more important is that the nonlocal processing approach can induce a couple of adjoint dilation and erosion, and combinations of them can construct adaptive opening, closing, alternating sequential filters, etc. By designing the new amoeba kernel, a family of morphological filters therefore is derived. Finally, this paper presents a series of results on both synthetic and real images along with comparisons with current state-of-the-art techniques, including novel applications to medical image processing and noisy SAR image restoration.