Physics

Channel (broadcasting)

Digital holography

Fourier transform

Gene

Speckle pattern

Optics

Holographic interferometry

Holography

Interferometry

Electronic speckle pattern interferometry

Speckle noise

Chemistry

Interference (communication)

Computer science

Phase (matter)

Digital holographic microscopy

Quantum mechanics

Biochemistry

Robustness (evolution)

Telecommunications

Phase retrieval

Speckle imaging

Applied Optics

In digital holographic interferometry, reliable estimation of phase derivatives from the complex interference field signal is an important challenge since these are directly related to the displacement derivatives of a deformed object. In this paper, we propose an approach based on deep learning for direct estimation of phase derivatives in digital holographic interferometry. Using a Y-Net model, our proposed approach allows for simultaneous estimation of phase derivatives along the vertical and horizontal dimensions. The robustness of the proposed approach for phase derivative extraction under both additive white Gaussian noise and speckle noise is shown via numerical simulations. Subsequently, we demonstrate the practical utility of the method for deformation metrology using experimental data obtained from digital holographic interferometry.

Phase derivative estimation in digital holographic interferometry using a deep learning approach