Learning Wavefront Coding for Extended Depth of Field Imaging

TL;DR

This paper introduces a novel computational imaging method combining wavefront coding with neural network deblurring to achieve superior extended depth of field imaging, optimizing optical design and post-processing jointly.

Contribution

It presents an end-to-end differentiable framework that jointly optimizes the optical element and deblurring network for improved EDoF imaging performance.

Findings

Achieves superior EDoF imaging with minimal artifacts.

Demonstrates effectiveness in deep 3D scenes and broadband imaging.

Provides an analytical expression for the DOE search space.

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.