1 research outputs found
Fast Autofocusing using Tiny Transformer Networks for Digital Holographic Microscopy
The numerical wavefront backpropagation principle of digital holography
confers unique extended focus capabilities, without mechanical displacements
along z-axis. However, the determination of the correct focusing distance is a
non-trivial and time consuming issue. A deep learning (DL) solution is proposed
to cast the autofocusing as a regression problem and tested over both
experimental and simulated holograms. Single wavelength digital holograms were
recorded by a Digital Holographic Microscope (DHM) with a 10
microscope objective from a patterned target moving in 3D over an axial range
of 92 m. Tiny DL models are proposed and compared such as a tiny Vision
Transformer (TViT), tiny VGG16 (TVGG) and a tiny Swin-Transfomer (TSwinT). The
experiments show that the predicted focusing distance is
accurately inferred with an accuracy of 1.2 m in average in comparison
with the DHM depth of field of 15 m. Numerical simulations show that all
tiny models give the with an error below 0.3 m. Such
a prospect would significantly improve the current capabilities of computer
vision position sensing in applications such as 3D microscopy for life sciences
or micro-robotics. Moreover, all models reach state of the art inference time
on CPU, less than 25 ms per inference