Researchers at King Abdullah University of Science and Technology (KAUST) have developed an image-processing algorithm for enhanced 3D particle-tracking.
By utilising a straightforward camera system combined with an advanced image-processing algorithm, researchers have been able further particle-tracking efforts.
3D particle-tracking system
The team traded in a complex hardware setup for a simpler one and paired it with optimised image processing in order to develop the quicker and more reliable 3D particle-tracking system.
The ability to examine the 3D movement of particles in flow is essential to studies of aerodynamics, fluid flow and molecular dynamics. Typically, this is accomplished by utilising a complicated combination of various cameras, the images from which are studied and compared to reconstruct the motion of individual particles in 3D space over time. However, as a result of the complexity of the setup and the necessity for regular and particular calibration, 3D particle velocimetry systems are generally large, costly and challenging to use.
Digital holography
Holography presents a hopeful and simpler substitute. As part of this method, the particles are lit up with a laser beam and the particle image is depicted by a single camera. As the laser light diffracts around each particle, the 3D positioning of the particle can be quantified from the size of the diffraction ring in the image. However, although the hardware for this technique has been developed, the software for reconstructing the particle flow is in its early stages.
Now, Ni Chen and Congli Wang in Wolfgang Heidrich’s group at KAUST have created an optimised particle-motion reconstruction algorithm that may potentially increase the adoption of digital holographic particle velocimetry.
“Inline holography requires fewer components, has a much simpler setup, can be easily used with microscopes and offers a higher spatial resolution, but is harder to solve numerically,” commented Wang. “We have shown that we can achieve the same or even better performance than conventional methods by using sophisticated software algorithms.”
Previous particle-motion reconstruction algorithms examined particle location and motion in distinct sequential steps. The group established a numerical algorithm called Holo-Flow that resolves both location and motion in parallel, cross-feeding the data in each step. Not only does this enhance the precision and quality of the flow reconstruction, it also enables the algorithm processing to be parallelised for much quicker computation.
“This work shows the potential of computational image processing where the hardware and software are jointly considered as a whole for encoding and decoding target information,” added Wang. “Using this method with a simple inline holography setup, we can reconstruct a flow field in a few seconds instead of hours on a single graphics processor.”
