A New Quality Map for 2-D Phase Unwrapping Based on Gray Level Co-Occurrence Matrix

Both in quality-guide phase unwrapping algorithms and weighted minimum-norm phase unwrapping algorithms, quality maps play a crucial role in obtaining the absolute phase from the wrapped ones. In this letter, a new technique for generating quality maps based on the gray level co-occurrence matrix (GLCM) is proposed. GLCM is a classical second-order statistics method for analyzing the texture features of images. Through exploring the second-order statistics of GLCM, much useful information in the image can be exploited. According to the characteristics of the interferogram, the second-order statistic of GLCM called “difference of entropy” is used to generate the quality maps. Besides, we modified the definition of “difference of entropy” to make the statistic more suitable for the problem. Finally, the new algorithm is compared with other conventional algorithms both in the simulated and real data experiments and the results show its better performance.