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Learning Wavefront Coding for Extended Depth of Field Imaging.

Authors :
Akpinar, Ugur
Sahin, Erdem
Meem, Monjurul
Menon, Rajesh
Gotchev, Atanas
Source :
IEEE Transactions on Image Processing. 2021, Vol. 30, p3307-3320. 14p.
Publication Year :
2021

Abstract

Depth of field is an important factor of imaging systems that highly affects the quality of the acquired spatial information. Extended depth of field (EDoF) imaging is a challenging ill-posed problem and has been extensively addressed in the literature. We propose a computational imaging approach for EDoF, where we employ wavefront coding via a diffractive optical element (DOE) and we achieve deblurring through a convolutional neural network. Thanks to the end-to-end differentiable modeling of optical image formation and computational post-processing, we jointly optimize the optical design, i.e., DOE, and the deblurring through standard gradient descent methods. Based on the properties of the underlying refractive lens and the desired EDoF range, we provide an analytical expression for the search space of the DOE, which is instrumental in the convergence of the end-to-end network. We achieve superior EDoF imaging performance compared to the state of the art, where we demonstrate results with minimal artifacts in various scenarios, including deep 3D scenes and broadband imaging. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
10577149
Volume :
30
Database :
Academic Search Index
Journal :
IEEE Transactions on Image Processing
Publication Type :
Academic Journal
Accession number :
170077701
Full Text :
https://doi.org/10.1109/TIP.2021.3060166