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Journal Articles Applied Sciences Year : 2023

PSDFH: A Phase-Space-Based Depth from Hologram Extraction Method


Object pre-localization from computer-generated holograms is still an open problem in the current state of the art. In this work, we propose the use of the hologram phase space representation to determine a set of regions of interest where the searched object can be located. The extracted regions can be used to pre-locate the object in 3D space and are further refined to produce a more accurate depth estimate. An iterative refinement method is proposed for 1D holograms and is extended in a parsimonious version for 2D holograms. A series of experiments are conducted to assess the quality of the extracted regions of interest and the sparse depth estimate produced by the iterative refinement method. Experimental results show that it is possible to pre-localize the object in 3D space from the phase space representation and thus to improve the calculation time by reducing the number of operations and numerical reconstructions necessary for the application of s (DFF) methods. Using the proposed methodology, the time for the application of the DFF method is reduced by half, and the accuracy is increased by a factor of three.
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Dates and versions

hal-03997534 , version 1 (20-02-2023)



Nabil Madali, Antonin Gilles, Patrick Gioia, Luce Morin. PSDFH: A Phase-Space-Based Depth from Hologram Extraction Method. Applied Sciences, 2023, 13 (4), pp.2463. ⟨10.3390/app13042463⟩. ⟨hal-03997534⟩
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