Abstract
This paper presents an algorithm for automatic detection of erroneous amplitude and phase components of a sample’s optical field, acquired by a holographic tomograph with a limited angle of projection. By applying image processing methods and statistical analysis to find and remove unfit projections, the quality of tomographic reconstruction of a 3D refractive index distribution of an object is greatly improved. The proposed methods can find their application in preprocessing of data in holographic tomography. Full Text: PDF ReferencesA. Kuś, W. Krauze, P. L. Makowski, and M. Kujawińska, "Holographic tomography: hardware and software solutions for 3D quantitative biomedical imaging (Invited paper)", ETRI Journal, 41, 1 (2019). CrossRef V. Balasubramani et al., "Phase unwrapping in ICF target interferometric measurement via deep learning", Appl. Opt., 60, 10 (2021). CrossRef Y. Park, C. Depeursinge, and G. Popescu, "Quantitative phase imaging in biomedicine", Nature Photonics, 12, 10 (2018). CrossRef W. Krauze, P. Makowski, M. Kujawińska, and A. Kuś, "Generalized total variation iterative constraint strategy in limited angle optical diffraction tomography", Opt. Express, 24, 5 (2016). CrossRef D. Ryu et al., "A non-calorimetric approach for investigating the moisture-induced ageing of a pyrotechnic delay material using spectroscopies", Sci Rep, 9, 1 (2019). CrossRef B. S. Lipkin, Picture Processing and Psychopictorics. (Saint Louis, Elsevier Science 2014). DirectLink A. M. Taddese, N. Verrier, M. Debailleul, J.-B. Courbot, and O. Haeberlé, "Optimizing sample illumination scanning in transmission tomographic diffractive microscopy", Appl. Opt., 60, 6 (2021). CrossRef
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