Solving the twin image problem in in-line holography by using multiple defocused intensity images reconstructed from a single hologram

Gnetto, Marius Ipo; Kossonou, Yao Taky Alvarez; Koffi, Yao; Kaduki, Kenneth A.; Zoueu, Jérémie T.

doi:10.1080/09500340.2021.2011450

Cited by 5 publications

(2 citation statements)

References 30 publications

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“…The authors propose a simple negative segmentation mask containing the support region which, when multiplied with the conjugate plane field, sets any pixels within the support region to zero. Following this masking operation, the updated conjugate plane is propagated back An alternative direct method was proposed by Gnetto et al in 2022, 12 based on a Transport-of-Intensity Equation 13-15 (TIE) modality. TIE is an established method of phase retrieval and is based on the inherent relationship that exists between the intensity variations of an optical field along its axis of propagation, and the phase of the field in perpendicular planes.…”

Section: Direct Holographic Reconstruction Methodsmentioning

confidence: 99%

Review of the state-of-the-art of in-line holographic microscopy

Madsen,

Panah,

Larsen

et al. 2023

Complex Light and Optical Forces XVII

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Section: Direct Holographic Reconstruction Methodsmentioning

confidence: 99%

Review of the state-of-the-art of in-line holographic microscopy

Madsen,

Panah,

Larsen

et al. 2023

Complex Light and Optical Forces XVII

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“…In the past, different methods have been proposed to tackle reconstruction of the in-line hologram, including the phase retrieval algorithms 60 – 65 . Accurate recovery of the phase can lead to strong suppression of the twin image 66 , 67 . We use an unsupervised DL approach for reconstructing the in-line hologram using an encoder–decoder architecture 17 .…”

Section: Experimental Recordingmentioning

confidence: 99%

Randomness assisted in-line holography with deep learning

Manisha

Mandal

Rathor

et al. 2023

Sci Rep

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We propose and demonstrate a holographic imaging scheme exploiting random illuminations for recording hologram and then applying numerical reconstruction and twin image removal. We use an in-line holographic geometry to record the hologram in terms of the second-order correlation and apply the numerical approach to reconstruct the recorded hologram. This strategy helps to reconstruct high-quality quantitative images in comparison to the conventional holography where the hologram is recorded in the intensity rather than the second-order intensity correlation. The twin image issue of the in-line holographic scheme is resolved by an unsupervised deep learning based method using an auto-encoder scheme. Proposed learning technique leverages the main characteristic of autoencoders to perform blind single-shot hologram reconstruction, and this does not require a dataset of samples with available ground truth for training and can reconstruct the hologram solely from the captured sample. Experimental results are presented for two objects, and a comparison of the reconstruction quality is given between the conventional inline holography and the one obtained with the proposed technique.

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