Affiliation:
1. The Chinese University of Hong Kong
2. Centre for Perceptual and Interactive Intelligence
Abstract
Many existing polarization networks reconstruct polarization information based on calculating the angle of polarization (AoP) loss. Yet, the conventional loss calculation method, which is based on a linear difference approach, compromises the reconstruction accuracy and causes additional training time when combined with learning-based methods. In this Letter, we present a new, to the best of our knowledge, method to calculate the AoP loss and apply it in an enhanced color polarization demosaicking network with a “multi-branch” structure, i.e., ePDNet. Experiments are performed to demonstrate the efficacy and superiority of the method, which improves the network convergence speed by three times as well as the output image quality. The new method may find important applications in the field of polarimetric imaging.
Funder
Innovation and Technology Commission
Centre for Perceptual and Interactive Intelligence (CPII) Ltd. under the Innovation and Technology Fund
Subject
Atomic and Molecular Physics, and Optics
Cited by
21 articles.
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