Self-Calibrating Imaging Polarimetry

To map the polarization state (Stokes vector) of objects in a scene, images are typically acquired using a polarization filter (analyzer), set at different orientations. Usually these orientations are assumed to be all known. Often, however, the angles are unknown: most photographers manually rotate the filter in coarse undocumented angles. Deviations in motorized stages or remote-sensing equipment are caused by device drift and environmental changes. This work keeps the simplicity of uncontrolled uncalibrated photography, and still extracts from the photographs accurate polarimetry. This is achieved despite unknown analyzer angles and the objects´ Stokes vectors. The paper derives modest conditions on the data size, to make this task well-posed and even over-constrained. The paper then proposes an estimation algorithm, and tests it in real experiments. The algorithm demonstrates high accuracy, speed, simplicity and robustness to strong noise and other signal disruptions.