Recent advances in speckle decorrelation modeling and processing in digital holographic interferometry


  • Pascal Picart Le Mans University



digital holography, speckle, noise modelling, noise processing, deep learning,


Digital holography, and especially digital holographic interferometry, is a powerful approach for the characterization of modifications at the surface or in the volume of objects. Nevertheless, the reconstructed phase data from holographic interferometry is corrupted by the speckle noise. In this paper, we discuss on recent advances in speckle decorrelation noise removal. Two main topics are considered. The first one presents recent results in modelling the decorrelation noise in digital Fresnel holography. Especially the anisotropy of the decorrelation noise is established. The second topic presents a new approach for speckle de-noising using deep convolution neural networks.

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How to Cite

P. Picart, “Recent advances in speckle decorrelation modeling and processing in digital holographic interferometry”, Photonics Lett. Pol., vol. 13, no. 4, pp. 73–75, Dec. 2021.