人工智能
计算机科学
块(置换群论)
计算机视觉
相位展开
图像处理
相(物质)
图像(数学)
噪音(视频)
算法
模式识别(心理学)
干涉测量
数学
光学
物理
有机化学
化学
几何学
作者
J. Strand,Torfinn Taxt,Anil K. Jain
出处
期刊:IEEE transactions on image processing
[Institute of Electrical and Electronics Engineers]
日期:1999-03-01
卷期号:8 (3): 375-386
被引量:71
摘要
We present a block least-squares (BLS) method for two-dimensional (2-D) phase unwrapping. The method works by tessellating the input image into small square blocks with only one phase wrap. These blocks are unwrapped using a simple procedure, and the unwrapped blocks are merged together using one of two proposed block merging algorithms. By specifying a suitable mask, the method can easily handle objects of any shape. This approach is compared with the Ghiglia-Romero (1994) method and the Marroquin-Rivera (1995) method. On synthetic images with different noise levels, the BLS method is shown to be superior, both with respect to the resulting gray values in the unwrapped image as well as visual inspection. The method is also shown to successfully unwrap synthetic and real images with shears, fiber-optic interferometry images, and medical magnetic resonance images. We believe the new method has the potential to improve the present quality of phase unwrapped images of several different image modalities.
科研通智能强力驱动
Strongly Powered by AbleSci AI