Comparison of model-based and data-based co-design of phase masks for depth of field extension

Pauline Trouvé-Peloux; Alice Fontbonne; Marius Dufraisse; Frédéric Champagnat

doi:10.1117/12.3017069

18 June 2024 Comparison of model-based and data-based co-design of phase masks for depth of field extension

Pauline Trouvé-Peloux, Alice Fontbonne, Marius Dufraisse, Frédéric Champagnat

Proceedings Volume 12996, Unconventional Optical Imaging IV; 1299608 (2024) https://doi.org/10.1117/12.3017069
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Abstract

Co-design consists of optimizing the parameters of the lens and the processing together to obtain a gain in performance for the entire optical/processing chain, which requires new optimization tools, as traditional optical design ones can no longer be used easily. In the state of the art, joint optical/processing optimization methods based on statistical scene and blur models and analytical performance models have been proposed, and recently, new approaches based on the joint optimization of a neural network and optical components with a large database have been developed. However, to the best of our knowledge, no comparison of co-design results using either a model-based or a data-based approach for the same task have been conducted, which is the scope of this paper. We consider here the optimization of phase masks to extend the camera depth of field. We compare the optimization results using a performance model based on restoration error using either a generalized Wiener filter, or a neural network. We investigate the optimization trend depending on the neural network complexity, the starting point of the optimization and the possible interaction between the two approaches.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Pauline Trouvé-Peloux, Alice Fontbonne, Marius Dufraisse, and Frédéric Champagnat "Comparison of model-based and data-based co-design of phase masks for depth of field extension", Proc. SPIE 12996, Unconventional Optical Imaging IV, 1299608 (18 June 2024); https://doi.org/10.1117/12.3017069

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
8 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Model based design

Data modeling

Mathematical optimization

Signal filtering

Tunable filters

Image processing

Neural networks

Show All Keywords

Keywords/Phrases

Search In:

Publication Years