Shape and deformation measurements of rough surfaces by phase-shifting digital holography

In digital holography recording as reconstruction of holograms are performed digitally by modern photonic devices to increase of optical non-contacting measurements of various kinds of surfaces including both specular and rough surfaces. In this article we discusses these features of digital holography using phase shifting techniques that has much extended its capabilities. Full Text: PDF ReferencesG. Bruning, D.R. Herriott, J.E. Gallagher, D.P. Rosenfeld, A.D. White, D.J. Brangaccio, "Digital Wavefront Measuring Interferometer for Testing Optical Surfaces and Lenses", Appl. Opt. 13, 2693 (1974). CrossRef I. Yamaguchi, T. Zhang, "Phase-shifting digital holography", Opt. Lett. 22, 1268 (1997). CrossRef F. Zhang, I. Yamaguchi, L.P. Yaroslavsky, "Algorithm for reconstruction of digital holograms with adjustable magnification", Opt. Lett. 29, 1668 (2004). CrossRef I. Yamaguchi, "Holography, speckle, and computers", Optics and Lasers in Engineering 39, 411 (2003). CrossRef I. Yamaguchi, M. Yokota, "Speckle noise suppression in measurement by phase-shifting digital holography", Opt. Eng. 48 085602 (2009). CrossRef I. Yamaguchi, J. Kato, S. Ohta, "Surface Shape Measurement by Phase-Shifting Digital Holography", Opt. Rev. 8, 85 (2001). CrossRef I. Yamaguchi, J. Kato, H. Matsuzaki, "Measurement of surface shape and deformation by phase-shifting image digital holography", Opt. Eng. 42, 1267 (2003). CrossRef F. Zhang, J.D.R. Valera, I. Yamaguchi, M. Yokota, G. Mills, "Vibration Analysis by Phase Shifting Digital Holography", Opt. Rev. 11, 5 (2004). CrossRef

Multi-Incidence Holographic Profilometry for Large Gradient Surfaces with Sub-Micron Focusing Accuracy

Surface reconstruction for micro-samples with large discontinuities using digital holography is a challenge. To overcome this problem, multi-incidence digital holographic profilometry (MIDHP) has been proposed. MIDHP relies on the numerical generation of the longitudinal scanning function (LSF) for reconstructing the topography of the sample with large depth and high axial resolution. Nevertheless, the method is unable to reconstruct surfaces with large gradients due to the need of: (i) high precision focusing that manual adjustment cannot fulfill and (ii) preserving the functionality of the LSF that requires capturing and processing many digital holograms. In this work, we propose a novel MIDHP method to solve these limitations. First, an autofocusing algorithm based on the comparison of shapes obtained by the LSF and the thin tilted element approximation is proposed. It is proven that this autofocusing algorithm is capable to deliver in-focus plane localization with submicron resolution. Second, we propose that wavefield summation for the generation of the LSF is carried out in Fourier space. It is shown that this scheme enables a significant reduction of arithmetic operations and can minimize the number of Fourier transforms needed. Hence, a fast generation of the LSF is possible without compromising its accuracy. The functionality of MIDHP for measuring surfaces with large gradients is supported by numerical and experimental results.

Phase-shifting digital holography with an unknown reference beam

In digital holography, errors of the reference field degrade the quality of the reconstructed object field. In this paper, we propose an effective method in phase-shifting digital holography in which the reference field does not need to be known and perfect. The unknown complex amplitudes of both reference and object fields are derived simultaneously. The method employs only five digital holograms and a single execution of a phase retrieval algorithm. So, the required measurements and algorithm executions in this method are fewer than those in other methods; it suggests a simpler and faster method. The effectiveness of the suggested method is indicated by simulation, under noise-free and noisy conditions. Moreover, the capability of the method to extract full information about the phase singularities in both fields is demonstrated.

Digital Hologram Watermarking Based on Multiple Deep Neural Networks Training Reconstruction and Attack

This paper proposes a method to embed and extract a watermark on a digital hologram using a deep neural network. The entire algorithm for watermarking digital holograms consists of three sub-networks. For the robustness of watermarking, an attack simulation is inserted inside the deep neural network. By including attack simulation and holographic reconstruction in the network, the deep neural network for watermarking can simultaneously train invisibility and robustness. We propose a network training method using hologram and reconstruction. After training the proposed network, we analyze the robustness of each attack and perform re-training according to this result to propose a method to improve the robustness. We quantitatively evaluate the results of robustness against various attacks and show the reliability of the proposed technique.

Single-Element Reflective Digital Holographic Microscopy

Digital holographic microscopy (DHM) is a well-known microscopy technique using an interferometric architecture for quantitative phase imaging (QPI) and it has been already implemented utilizing a large number of interferometers. Among them, single-element interferometers are of particular interest due to its simplicity, stability, and low cost. Here, we present an extremely simple common-path interferometric layout based on the use of a single one-dimensional diffraction grating for both illuminating the sample in reflection and generating the digital holograms. The technique, named single-element reflective digital holographic microscopy (SER-DHM), enables QPI and topography analysis of reflective/opaque objects using a single-shot operation principle. SER-DHM is experimentally validated involving different reflective samples.

Quantitative Phase Imaging Using Deep Learning-Based Holographic Microscope

Digital holographic microscopy enables the measurement of the quantitative light field information and the visualization of transparent specimens. It can be implemented for complex amplitude imaging and thus for the investigation of biological samples including tissues, dry mass, membrane fluctuation, etc. Currently, deep learning technologies are developing rapidly and have already been applied to various important tasks in the coherent imaging. In this paper, an optimized structural convolution neural network PhaseNet is proposed for the reconstruction of digital holograms, and a deep learning-based holographic microscope using above neural network is implemented for quantitative phase imaging. Living mouse osteoblastic cells are quantitatively measured to demonstrate the capability and applicability of the system.