解决多模光纤弯曲后图像重建的测量矩阵未知问题
Mismatch reconstruction theory for unknown measurement matrix in imaging through multimode fiber bending
- 提出失配方程与校准算法，重建缺失的测量矩阵
- 低噪声下可成功复原原始图像，且对噪声有一定鲁棒性
- 适合光纤成像系统在动态弯曲场景下的实时图像恢复
多模光纤成像依赖测量值与测量矩阵的精确匹配，但实际应用中因系统配置未知或光纤任意弯曲导致矩阵无法获取，传统重建算法失效。本文提出一种新型失配重建理论，通过构建失配方程并设计匹配与校准算法，实现新测量矩阵的构造。实验表明，在低噪声条件下，重构矩阵可作为匹配对用于传统重建算法，成功恢复原始图像。进一步分析了噪声、计算精度和正交性对重建性能的影响，结果表明所提算法具备一定鲁棒性。附录提供了完整理论推导。代码已开源：https://github.com/yanglebupt/mismatch-solution。
原文摘要 · Abstract (English)
Multimode fiber imaging requires strict matching between measurement value and measurement matrix to achieve image reconstruction. However, in practical applications, the measurement matrix often cannot be obtained due to unknown system configuration or difficulty in real-time alignment after arbitrary fiber bending, resulting in the failure of traditional reconstruction algorithms. This paper presents a novel mismatch reconstruction theory for solving the problem of image reconstruction when measurement matrix is unknown. We first propose mismatch equation and design matched and calibration solution algorithms to construct a new measurement matrix. In addition, we also provide a detailed proof of these equations and algorithms in the appendix. The experimental results show that under low noise levels, constructed matrix can be used for matched pair in traditional reconstruction algorithms, and reconstruct the original image successfully. Then, we analyze the impact of noise, computational precision and orthogonality on reconstruction performance. The results show that proposed algorithms have a certain degree of robustness. Finally, we discuss the limitations and potential applications of this theory. The code is available: https://github.com/yanglebupt/mismatch-solution.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。