Self-Supervised Focus Measure Fusing for Depth Estimation from Computer-Generated Holograms

Nabil Madali (1, 2) , Antonin Gilles (1) , Patrick Gioia (3, 1) , Luce Morin (4, 2)

1 Advanced Media Content
2 IETR - Institut d'Électronique et des Technologies du numéRique
3 Orange Labs R&D [Rennes]
4 INSA Rennes - Institut National des Sciences Appliquées - Rennes

Depth from focus is a simple and effective methodology for retrieving the scene geometry from a hologram when used with the appropriate focus measure and patch size. However, fixing those parameters for every sample may not be the right choice, as different scenes can be composed with various types of textures. In this work, we propose a self-supervised learning methodology for fusing the depth maps produced using different focus measures with variable patch sizes applied to the holographic reconstruction volume. Experimental results show that fusing depth information produces more accurate and smoother depth maps, which can be directly used for alternative tasks such as motion estimation.

Keywords: 3D Imaging; Holography; Depth Estimation; Self-supervised learning; Depth from focus

Domains: Image Processing [eess.IV]

Nabil Madali, Antonin Gilles, Patrick Gioia, Luce Morin. Self-Supervised Focus Measure Fusing for Depth Estimation from Computer-Generated Holograms. 2023 IEEE International Conference on Image Processing (ICIP 2023), Oct 2023, Kuala Lumpur, Malaysia. pp.2285-2289, ⟨10.1109/ICIP49359.2023.10221949⟩. ⟨hal-04227451⟩