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Residues Cluster-Based Segmentation and Outlier-Detection Method for Large-Scale Phase Unwrapping.
- Source :
- IEEE Transactions on Image Processing; Oct2011, Vol. 20 Issue 10, p2865-2875, 11p
- Publication Year :
- 2011
-
Abstract
- 2-D phase unwrapping is an important technique in many applications. However, with the growth of image scale, how to tile and splice the image effectively has become a new challenge. In this paper, the phase unwrapping problem is abstracted as solving a large-scale system of inconsistent linear equations. With the difficulties of large-scale phase unwrapping analyzed, L^0-norm criterion is found to have potentials in efficient image tiling and splicing. Making use of the clustering characteristic of residue distribution, a tiling strategy is proposed for L^0-norm criterion. Unfortunately, L^0-norm is an NP-hard problem, which is very difficult to find an exact solution in a polynomial time. In order to effectively solve this problem, equations corresponding to branch cuts of L^0-norm in the inconsistent equation system mentioned earlier are considered as outliers, and then an outlier-detection-based phase unwrapping method is proposed. Through this method, a highly accurate approximate solution to this NP-hard problem is achieved. A set of experimental results shows that the proposed approach can avoid the inconsistency between local and global phase unwrapping solutions caused by image tiling. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10577149
- Volume :
- 20
- Issue :
- 10
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Image Processing
- Publication Type :
- Academic Journal
- Accession number :
- 65935186
- Full Text :
- https://doi.org/10.1109/TIP.2011.2138148