Scene-based semi-supervised algorithm for self-calibration of polarization imaging systems.

To address the demand for convenient and high-accuracy calibration of modern polarization imaging systems, this study proposes a scene-based semi-supervised self-calibration algorithm. The algorithm fully exploits the polarization information perceived by the system, constructs three loss functions based on the light intensity, degree of linear polarization (DoLP), and angle of polarization (AoP), and employs a hybrid multi-objective optimization method that combines an evolutionary algorithm and gradient descent to achieve self-calibration. Compared to supervised algorithms, the proposed method uses scene information to match the image and obtain the rotation angle as the ground truth of the AoP variation. Combining the high accuracy of supervised optimization with the advantage of unsupervised optimization that requires no ground truth, the algorithm is convenient to use, achieves high accuracy, and is suitable for various four-channel polarization imaging systems.