Sparse reconstruction for integral Fourier holography using dictionary learning method

Lakshmi Kuruguntla, Vineela Chandra Dodda, Min Wan, Karthikeyan Elumalai, Sunil Chinnadurai, Inbarasan Muniraj, John T. Sheridan

Research output: Contribution to journalArticlepeer-review


A simplified (i.e., single shot) method is demonstrated to generate a Fourier hologram from multiple two-dimensional (2D) perspective images (PIs) under low light level imaging conditions. It was shown that the orthographic projection images (OPIs) can be synthesized using PIs and then, following incorporation of corresponding phase values, a digital hologram can be generated. In this work, a fast dictionary learning (DL) technique, known as Sequential Generalised K-means (SGK) algorithm, is used to perform Integral Fourier hologram reconstruction from fewer samples. The SGK method transforms the generated Fourier hologram into its sparse form, which represented it with a linear combination of some basis functions, also known as atoms. These atoms are arranged in the form of a matrix called a dictionary. In this work, the dictionary is updated using an arithmetic average method while the Orthogonal Matching Pursuit algorithm is opted to update the sparse coefficients. It is shown that the proposed DL method provides good hologram quality, (in terms of peak signal-to-noise ratio) even for cases of ~ 90% sparsity.
Original languageEnglish (US)
Issue number6
StatePublished - May 29 2022
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2022-06-15
Acknowledgements: Authors sincerely thank Dr. Ni Chen of Visual Computing Center, King Abdullah University of Science and Technology (KAUST), for providing the integral hologram dataset. LK, VC, KE, and SC acknowledges the support of SRM University AP research fund. IM acknowledges the Science and Engineering Research Board (SERB) under SRG/2021/001464, Department of Science and Technology, Government of India. MW acknowledges CELTA (675683) in the Horizon 2020 programme support.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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