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 SpatiallyVariant 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.

Region-Based Segmentation and Wiener Pilot-Based Novel Amoeba Denoising Scheme for CT Imaging

“…The proposed scheme is more robust and performs significantly better than the classical amoeba filtering in the presence of projection noise. The scheme inherits significant image noise reduction, Gaussian, and otherwise, due to dependence on Wiener and Amoeba filtering [ 22 , 26 , 30 ]. The superiority of Wiener filter pilot over Gaussian filter pilot corresponds to lesser error floor in case of increased noise levels; while the similarity is increased mainly because of the region-based approach of the scheme matching the human anatomy.…”

“…The Wiener filter is much superior in image denoising and restoration than many other techniques, such as simple inverse filtering, Gaussian filtering, and mean filtering [ 30 ]. Therefore, the Wiener filter-based pilot image suppresses noise while preserving the image details, unlike the Gaussian-based pilot in classical amoeba and its variants [ 22 , 26 , 30 ]. Furthermore, the presented work also improves the methodology for amoeba shape acquisition by replacing the classical amoeba distance-based approach with region-based segmentation.…”

Adaptive Anisotropic Morphological Filtering Based on Co-Circularity of Local Orientations

“…The need to analyze and filter shapes without corrupting them naturally gave rise to the so called adaptive or spatially variant mathematical morphology [6,7,15,26], in which structuring elements could vary in space so as to adapt to the local structures of the images being processed. This framework was applied to design edge-preserving filters in general [9,10,18,33], but also showed to be particularly relevant to the processing of thin anisotropic objects in images [29,32], such as vessels, fibers or roads in satellite images.…”

Patch-Based Mathematical Morphology for Image Processing, Segmentation and Classification

“…Then, Velasco-Forero and Angulo [25] have recasted the works of Salembier in the general framework of adaptive MM and presented their necessary properties to be considered as algebraic MM operators. Recently, in [26] Yang and Li have considered a new type of adaptive SE based on amoeba SE combining local geodesic distance and non-local patch distance for spatially variant morphological filters.…”