# Learning Wavefront Coding for Extended Depth of Field Imaging

arXiv: https://arxiv.org/abs/1912.13423 · HTML: https://mlsanity.com/paper/1912.13423/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2019.

## Built on by (most cited first, 6 of 6)

- [Curriculum Learning for ab initio Deep Learned Refractive Optics](https://mlsanity.com/paper/2302.01089/) (2023)
- [Video Reconstruction from a Single Motion Blurred Image using Learned Dynamic Phase Coding](https://mlsanity.com/paper/2112.14768/) (2021)
- [Depth-Aware Image Compositing Model for Parallax Camera Motion Blur](https://mlsanity.com/paper/2303.09334/) (2023)
- [Deep Phase Coded Image Prior](https://mlsanity.com/paper/2404.03906/) (2024)
- [Super-resolution image projection over an extended depth of field using a diffractive decoder](https://mlsanity.com/paper/2510.03938/) (2025)
- [Wavefront Coding for Accommodation-Invariant Near-Eye Displays](https://mlsanity.com/paper/2510.12778/) (2025)

## Abstract (arXiv)

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.