Journal of the European Optical Society-Rapid Publications, Volume. 20, Issue 1, 2024032(2024)
Digital holographic microscopy applied to 3D computer micro-vision by using deep neural networks
Fig. 1. (a) Lyncee-tec DHM observing a micro-structured pattern moved by a hexapod stage. (b) A typical experimental hologram of a pseudo-periodic pattern that allow 3D pose measurement [2]. Image reconstruction (c) in amplitude and (d) in phase at a numerical in-focus distance of 185 μm.
Fig. 2. (a–c) Thumbnail reconstruction. (d–f) Assess the distance Z. (a) A ROI of 768 × 768 is cropped from the hologram at a fixed position. (b) XY Model (based on a UNet like model). (c) The reconstructed thumbnail of 64 × 64 pixels. (d) A ROI of 128 × 128 is randomly cropped from the hologram space. (e) Z model based on an adapted version of a GedankenNet model [3]. (f) The distance Z.
Fig. 3. (a) Outliers (in red), simulated (in blue) and estimated (in green) trajectory in the 3D space. (b) Z and X-Y errors in μm (absolute difference and L2-norm). The Z error is mostly below an error of 1 μm (red dashed line).
Fig. 4. Matching rate associated to each 3D pose (red: outliers, green: right 3D poses).
Get Citation
Copy Citation Text
Stéphane Cuenat, Jesús E. Brito Carcaño, Belal Ahmad, Patrick Sandoz, Raphaël Couturier, Guillaume J. Laurent, Maxime Jacquot. Digital holographic microscopy applied to 3D computer micro-vision by using deep neural networks[J]. Journal of the European Optical Society-Rapid Publications, 2024, 20(1): 2024032
Category: Research Articles
Received: Jan. 31, 2024
Accepted: Jun. 13, 2024
Published Online: Dec. 16, 2024
The Author Email: Maxime Jacquot (maxime.jacquot@univ-fcomte.fr)