# An Improved Wavelet Modulus Algorithm Based on Fusion of Light Intensity and Degree of Polarization
); [EMAIL_ADDRESS] (J.T. ); [EMAIL_ADDRESS] (Y.Q. ); [EMAIL_ADDRESS] (W.W.)  
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Jingquayu  
1 State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130003, China; [EMAIL_ADDRESS] (Y.G. ); [EMAIL_ADDRESS] (J.B.); [EMAIL_ADDRESS] (B.Z. ); [EMAIL_ADDRESS] (K.Z. ); [EMAIL_ADDRESS] (K.Z. ); [EMAIL_ADDRESS] (J.T. ); [EMAIL_ADDRESS] (Y.Q. ); [EMAIL_ADDRESS] (W.W.)  
2  
Jingquayu  
1 State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China; [EMAIL_ADDRESS] (Y.G. ); [EMAIL_ADDRESS] (J.B.); [EMAIL_ADDRESS] (B.Z. ); [EMAIL_ADDRESS] (K.Z. ); [EMAIL_ADDRESS] (K.Z. ); [EMAIL_ADDRESS] (J.T. ); [EMAIL_ADDRESS] (Y.Q. ); [EMAIL_ADDRESS] (W.W.)  
2  
Jingquayu  
1 State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China; [EMAIL_ADDRESS] (Y.G. ); [EMAIL_ADDRESS] (J.B.); [EMAIL_ADDRESS] (B.Z. ); [EMAIL_ADDRESS] (K.Z. ); [EMAIL_ADDRESS] (Y.Z. ); [EMAIL_ADDRESS] (J.T. ); [EMAIL_ADDRESS] (Y.Q. ); [EMAIL_ADDRESS] (W.W.)  
2  
Jingquayu  
1 State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changa gradient image, and then selected the local maximum points in the obtained gradient image as the edge points [8]. Since then, edge-detection algorithms based on wavelet transform have developed continuously. Compared with the traditional edge-detection algorithm, edge-detection algorithms based on wavelet transform have good time-frequency domain characteristics and multi-scale analysis ability, and the edge detection effect is good. However, there are still many problems in these algorithms, which cannot completely extract the effective edge. Therefore, many scholars are committed to studying its improvement. In 2014, [PERSON] et al. proposed an improved wavelet modulus maxima algorithm that used the quadratic discriminant method to detect the local modulus maxima according to the edge and noise difference after wavelet transformation to achieve the separation of noise and edge. This algorithm can solve the problems of the high positioning accuracy and inconsistent denoising ability of traditional edge-detection algorithms [9]. In 2016, [PERSON] et al. proposed an algorithm that combines local blur enhancement, wavelet transform modulus maxima, and multi-scale products. The algorithm locates defect areas with low contrast in the HSI color space, and then performs local blur enhancement and calculation. A binary image of the defect edge of aluminum foil is obtained by the multi-scale product and wavelet transform. The algorithm can extract sharper and more complete edges [10]. In 2018, [PERSON] et al. proposed an image edge detection method based on wavelet transform energy features. The method has a faster edge extraction speed and can be used to solve the detection and recognition problems of objects in images in various information processing and control systems [11]. In 2020, [PERSON] et al. proposed a wavelet multi-scale edge-detection algorithm. The algorithm multiplies the wavelet coefficients by the adjacent scale products, and the local modulus maximum point of the scale product coefficients is determined as an edge. The algorithm can not only improve the detection performance, but also reduce the interaction between adjacent edges [12]. In 2021, [PERSON] et al. proposed a robust edge-detection algorithm based on a two-stage denoising system in a super-analytical wavelet transform domain. The algorithm can accurately extract edge information in the case of imposed white Gaussian noise or multiplicative speckle noise [13]. The current edge-detection algorithm based on wavelet transform mostly uses the gray-level change of the light intensity image to extract the edge.