Deep learning applied to quad-pixel plenoptic images

Guillaume Chataignier; Benoit Vandame; Jerome Vaillant

doi:10.1117/12.2597001

14 September 2021 Deep learning applied to quad-pixel plenoptic images

Guillaume Chataignier, Benoit Vandame, Jerome Vaillant

Proceedings Volume 11875, Computational Optics 2021; 1187505 (2021) https://doi.org/10.1117/12.2597001
Event: SPIE Optical Systems Design, 2021, Online Only

Abstract

In recent years, we have seen the development of integrated plenoptic sensors, where multiple pixels are placed under one microlens. It is mainly used by cameras and smartphones to drive the autofocus of the main lens, and it often takes the form of dual-pixels with 2 rectangular sub-pixels. We study the evolution of dual-pixels, the so-called quad-pixel sensor with 2x2 square sub-pixels under the microlens. As it is a simple light field capturing device, we investigate the computational photography abilities of such sensor. We first present our work on pixel-level simulations, then we present a model of image formation taking into account the diffraction by the microlens. Finally, we present new ways to process a quad-pixel images based on deep learning.

Conference Presentation

Citation Download Citation

Guillaume Chataignier, Benoit Vandame, and Jerome Vaillant "Deep learning applied to quad-pixel plenoptic images", Proc. SPIE 11875, Computational Optics 2021, 1187505 (14 September 2021); https://doi.org/10.1117/12.2597001

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