Polarization Image

Polarization imaging leverages the polarization properties of light to capture information beyond traditional intensity-based images, enabling improved scene understanding and object reconstruction. Current research focuses on developing novel algorithms and deep learning models, such as neural implicit surface reconstruction and polarization-to-polarization networks, to address challenges like reflection removal, despeckling in SAR images, and accurate 3D shape reconstruction from polarization data. These advancements are significantly impacting diverse fields, including medical imaging (e.g., cancer diagnosis), robotics (e.g., pose estimation), and material science, by providing richer, more robust data for analysis and improved computational methods for processing polarization images. The development of open-source toolkits further facilitates broader adoption and accelerates progress in this area.