Polarimetric imaging via deep learning

Haofeng Hu; Xiaobo Li; Tiegen Liu

doi:10.1117/12.2576856

10 October 2020 Polarimetric imaging via deep learning

Haofeng Hu, Xiaobo Li, Tiegen Liu

Proceedings Volume 11549, Advanced Optical Imaging Technologies III; 115491T (2020) https://doi.org/10.1117/12.2576856
Event: SPIE/COS Photonics Asia, 2020, Online Only

Conference Poster

Abstract

Deep learning is a powerful technique based on neural networks, which has distinct advantages in terms of finding the relation between the input and the label, and it has shown the superior performance for image recovery in complex and strong noise environment than other methods. We employ the deep learning method for polarimetric imaging in turbid media and in strong noise environment. For underwater imaging, the proposed learning-based method can effectively remove the veiling light and outperforms other existing methods, even in dense turbid water. For image denosing, the experimental results show that the proposed learning-based method has an evident performance on the noise suppression and outperforms other existing methods. Especially for the images of the degree of polarization and the angle of polarization, which are quite sensitive to the noise, the proposed learning-based method can well reconstruct the details flooded in strong noise.

Citation Download Citation

Haofeng Hu, Xiaobo Li, and Tiegen Liu "Polarimetric imaging via deep learning", Proc. SPIE 11549, Advanced Optical Imaging Technologies III, 115491T (10 October 2020); https://doi.org/10.1117/12.2576856

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available