Abstract
Polarimetric imaging is an effective way for clear vision in water, in which deducing the object radiance in clear water from the obtained polarimetric information in turbid water is essential. In this letter, we propose, for the first time to our knowledge, a learning-based method for polarimetric underwater image recovery. It is based on the dense network and can learn well the relation between the object radiance and the polarization information. The experimental results demonstrate that additionally introducing the polarization information is beneficial for improving the image quality. Moreover, the proposed learning-based method can effectively remove the veiling light and outperforms other existing methods, even in dense turbid water.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.