Edge detection is the basis of image analysis and image processing. The wavelet modulus maxima algorithm is a widely used edge-detection algorithm. The algorithm has the advantages of strong anti-noise ability and high precision of edge location, but it still cannot accurately obtain edge information for low-contrast images. Therefore, this paper proposes an improved wavelet mode maximum edge algorithm for the fusion of light intensity and degree of polarization. The improved wavelet mode maximum algorithm was used to extract the edges of a light intensity image and degree of polarization image, and then refine and fuse the two edges to obtain the final edge information. Simulation experiments showed that the edge image obtained by the edge-detection algorithm in this paper had a clearer outline and better connectivity.

We present a spatially modulated snapshot imaging polarimeter using two Savart polariscopes (SMSIPTS). Not only can it avoid alignment angle errors and additional phase errors of a half-wave plate (HWP), it can also avoid changing the HWP frequently when we want the target polarization state at different wavelengths and can increase some channel bandwidth to improve image quality, compared with a spatially modulated snapshot imaging polarimeter (SMSIP). The alignment angle error and additional phase errors of SMSIP and the optical layout and principle of SMSIPTS are derived first. The full Stokes polarization images can be obtained by processing the interferogram. Based on SMSIPTS, we determine the filtering method by simulation. We proved the feasibility of SMSIPTS, and the effect of SMSIPTS and SMSIP on reconstruction is compared by simulation. Last, we experimentally verified the feasibility of the theory of SMSIPTS.

Endoscopic inspection is an important non-destructive testing method. Traditional 3D endoscopic reconstruction methods, such as polarization reconstruction and shading reconstruction, have the drawbacks of not being able to determine the actual size and positional information of the object. The stereo vision method is limited by its own operating principles and has the issue sparse reconstructed point clouds. These drawbacks greatly limit the applications of the endoscope. Therefore, this work proposes a joint dense 3D reconstruction method for endoscopic imaging of weak texture scenes. This method uses the shading reconstruction normal to correct the polarization reconstruction normal, then uses coordinate conversion and point cloud fusion to convert the polarization and shading 3D reconstruction results from the pixel coordinate system to the world coordinate system. It combined the reconstruction results from the polarization, shading and stereo vision in the world coordinate system, and the fusion coefficients are obtained by solving the minimum error model; then, a complete and detailed 3D reconstruction surface was obtained in the world coordinate system. This method could avoid the difficulty of obtaining real coordinates for 3D reconstruction of polarization and shading and the issue of the sparse point cloud afforded by stereo vision reconstruction for weak texture scenes. Finally, a dense point cloud corresponding to each pixel in the world coordinate system could be obtained. The combined dense 3D reconstruction method had an average error of <1% for length measurement of a 3D curve, which is of high significance for industrial endoscopic inspection.