"Learning Wavefront Coding for Extended Depth of Field Imaging " accepted for publication

The article "Learning Wavefront Coding for Extended Depth of Field Imaging” by Ugur Akpinar, Erdem Sahin, Monjurul Meem, Rajesh Menon and Atanas Gotchev has been accepted for publication in a future issue of IEEE Transactions on Image Processing.

Figure from the article - the experimental results

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.

You can find the early access preprint here.