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A spatial minimum spanning tree filter.
- Source :
- Measurement Science & Technology; Jan2021, Vol. 32 Issue 1, p1-13, 13p
- Publication Year :
- 2021
-
Abstract
- It is well-known that the minimum spanning tree (MST) is widely used in image segment, edge-preserving filtering, and stereo matching. However, the non-local (NL) filter based on the MST generally results in overly smooth images, since it ignores spatial affinity. In this paper, we propose a new spatial minimum spanning tree filter (SMSTF) to improve the performance of the NL filter by designing a spatial MST to avoid over-smoothing problems, by introducing recursive techniques to implement the filtering process. The SMSTF has the advantages that: (1) the kernel of our filter considers spatial affinity and similarity of intensity; (2) The size of the filter kernel is the entire image domain; (3) the complexity of the SMSTF is linear to the number of image pixels. For these reasons, our filter achieves excellent edge-preserving results. Extensive experiments demonstrate the versatility of the proposed method in a variety of image processing and computer vision tasks, including edge-preserving smoothing, stylization, colorization, and stereo matching. [ABSTRACT FROM AUTHOR]
- Subjects :
- SPANNING trees
FILTERS & filtration
IMAGE processing
SPATIAL filters
PIXELS
Subjects
Details
- Language :
- English
- ISSN :
- 09570233
- Volume :
- 32
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Measurement Science & Technology
- Publication Type :
- Academic Journal
- Accession number :
- 147133267
- Full Text :
- https://doi.org/10.1088/1361-6501/abaa65