Super-resolution of photoelastic polarization images using a neural network and a divide-and-conquer strategy
Year: 2025
Type: article
Abstract: The use of polarization cameras in digital photoelasticity has become increasingly prevalent, significantly contributing to the advancement of digital photoelasticity . However, polarization images captured by polarization cameras suffer from resolution loss, which severely affects the accuracy of the isochromatics and isoclinics. Therefore, it is essential to enhance the resolution of these polarization images. In this study, a super-resolution reconstruction network specifically designed for photoelastic polarization images is proposed for the first time. A large dataset was generated and collected to support the development of this network. Additionally, a divide-and-conquer strategy is introduced to avoid interference between the different degradation models . This strategy also enables the efficient acquisition of tens of thousands of real-world datasets. Comparative experiments demonstrate that the proposed divide-and-conquer strategy significantly enhances the network's accuracy. In comparison with interpolation methods and existing networks, the proposed network consistently outperforms them, showing superior performance. This network effectively increases the resolution of photoelastic polarization images, leading to more accurate isochromatics and isoclinics.
Source: Optics and Lasers in Engineering
Authors: Huajian Zhang, Shuhai Jia, Mengyu Han, Zihan Lin, Longning Wang, Bo Wen
Institution: Xi'an Jiaotong University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,020
Award: G162697937
OpenAlex ID: W4409385359