Deep learning-based scattering removal of light field imaging

Light field imaging has shown significance in research fields for its high-temporal-resolution 3D imaging ability. However, in scenes of light field imaging through scattering, such as biological imaging in vivo and imaging in fog, the quality of 3D reconstruction will be severely reduced due to the scattering of the light field information. In this paper, we propose a deep learning-based method of scattering removal of light field imaging. In this method, a neural network, trained by simulation samples that are generated by light field imaging forward models with and without scattering, is utilized to remove the effect of scattering on light fields captured experimentally. With the deblurred light field and the scattering-free forward model, 3D reconstruction with high resolution and high contrast can be realized. We demonstrate the proposed method by using it to realize high-quality 3D reconstruction through a single scattering layer experimentally.

Automated summary

In this paper, a deep learning-based method for scattering removal in light field imaging is proposed, which enables high-quality 3D reconstruction and addresses the scattering issue in light field imaging.