# An Improved Wavelet Modulus Algorithm Based on Fusion of Light Intensity and Degree of Polarization
## 4 Summary  
In order to overcome the problems of poor adaptive ability and poor edge continuity of traditional wavelet modulus maxima algorithms, as well as the problem of little information for edge extraction using only light intensity images, we propose an improved wavelet modulus maxima algorithm based on the fusion of light intensity and degree of polarization. The algorithm uses the improved wavelet modulus maximum algorithm to extract the edges of the light intensity image and degree of polarization image, and then refines and fuses the light intensity edge and the degree of polarization edge to obtain the edge information with rich details. Compared with other edge-detection algorithms, we found that the edge image obtained by this algorithm had less noise, stronger edge continuity, and a higher amount of edge information through simulation experiments. Therefore, the edge image obtained by the edge-detection algorithm in this paper had a clearer outline and better connectivity. The research in this paper provides a reference for the edge detection of images with serious noise interference and low contrast. The objects in the experimental images in this paper were glass and liquid, and there may have been reflections inside them. Since the degree of polarization image had the characteristics of weakening strong light and strengthening weak light, the reflection of the degree of polarization image was more obvious than that of the total light intensity image, and a small amount of artifacts appeared in the edge detection results in this paper. Edge detection works better if the object in the experimental image is opaque. It should be pointed out that our algorithm combined the light intensity image with the polarization image. The object in the experimental image had a high degree of polarization, so it was convenient to extract the edge of the polarization image. However, for some objects with a low degree of polarization, there would be a lot of noise in the image of degree of polarization, and the difference between the edge and noise would not be obvious, so this method would not be suitable. In the future, we will analyze the relationship between the polarization characteristics of different objects and edge detection results.  
Conceptualization, Y.G. and J.L. ([PERSON]); methodology, Y.G., Y.Q. and J.B.; software, Y.G. and Y.Z.; validation, J.L. ([PERSON]) and B.Z.; formal analysis, Y.G. and W.W.; investigation, Y.G. and J.T.; writing--original draft preparation, Y.G.; writing--review and editing, J.L. ([PERSON]), K.Z. and J.B.; funding acquisition, J.L. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
Figure 9: Comparison of edge detection in noisy images.  
Funding: This research was funded by the National Natural Science Foundation of China (Grant numbers 61805239, 61627819, 61727818, and 61905240), the Jilin Scientific and Technological Development Program (Grant numbers 20190303063 SF and 20180201024 GX), and the Youth Innovation Promotion Association Foundation of the Chinese Academy of Sciences (Grant number 2018254), the National Key Research and Development Program of China (2019 YFB2006000), and the National Major Science and Technology Projects (2019 QZKK020802).  
Not applicable.  
Data Availability Statement: The study did not report any data.  
The authors declare no conflict of interest.