Deep learning speckle de-noising algorithms for coherent metrology: a review and a phase-shifted iterative scheme [Invited] - Archive ouverte HAL Access content directly
Journal Articles Journal of the Optical Society of America. A Optics, Image Science, and Vision Year : 2022

Deep learning speckle de-noising algorithms for coherent metrology: a review and a phase-shifted iterative scheme [Invited]

Abstract

We present a review of deep learning algorithms dedicated to the processing of speckle noise in coherent imaging. We focus on methods that specifically process de-noising of input images. Four main classes of applications are described in this review: optical coherence tomography, synthetic aperture radar imaging, digital holography amplitude imaging, and fringe pattern analysis. We then present deep learning approaches recently developed in our group that rely on the retraining of residual convolutional neural network structures to process decorrelation phase noise. The paper ends with the presentation of a new approach that uses an iterative scheme controlled by an input SNR estimator associated with a phase-shifting procedure.
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Dates and versions

hal-03819619 , version 1 (18-10-2022)

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Silvio Montresor, Marie Tahon, Pascal Picart. Deep learning speckle de-noising algorithms for coherent metrology: a review and a phase-shifted iterative scheme [Invited]. Journal of the Optical Society of America. A Optics, Image Science, and Vision, 2022, 39 (2), pp.A62. ⟨10.1364/JOSAA.444951⟩. ⟨hal-03819619⟩
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