Microstructural surface characterization of stainless and plain carbon steel using digital holographic microscopy

Pourvais, Yousef; Asgari, Pegah; Abdollahi, Pedram; Khamedi, Ramin; Moradi, Ali‐Reza

doi:10.1364/josab.34.000b36

Cited by 23 publications

(10 citation statements)

References 23 publications

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“…DHM is a noncontact, nondestructive and nonscanning technique for quantitative phase imaging, therefore, suitable for retrieving the morphology of biological samples and surface topography of reflective ones. 42 We have already applied DHM for 3D visualisation of myelin figures and their dynamics under thermal gradients, 43 changes in their environmental humidity, 44 studying microstructural surface characterisation, 45 evaluation of intergranular corrosion of stainless steel, 46,47 microparticle sedimentation, 48 and 3D characterisation of nanocomposites. 39,49 In DHM, phase information of an object is preserved through recording the interference between a laser beam passing through the object, so-called object beam and a beam from the same laser source, so-called reference beam, on a digital sensor.…”

Section: Introductionmentioning

confidence: 99%

Digital holographic microscopy of spiropyran‐based dynamic materials

Jamali,

Rad,

Razaghi

et al. 2023

Journal of Microscopy

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Spiropyran (SP)‐based dynamic materials undergo structural changes in response to external stimuli. In this paper, we show that digital holographic microscopy (DHM) is an effective candidate for characterisation of SPs (embedded in polymer matrices) and for monitoring of their dynamical changes. The polymer matrices are polylactic acid (PLA) and poly(methyl methacrylate) (PMMA) films, which are decorated with SPs and immobilised on graphene quantum dots (GQDs). GQDs are modified by benzylamines prior to the loading of SP species because of the enhancement of hydrophobic characteristics. UV irradiation is used as the external stimulus and the dynamical changes of the samples before and after UV irradiation are measured. DHM is arranged on a novel self‐referencing setup, which substantially reduces the sensitivity of DHM to environmental vibrations. Morphometric information for characterisation of the samples is obtained by analysis of the recorded digital holograms. The experimental results demonstrate the potential of the presented technique to serve as an alternative technique for surface measurement methodologies such as atomic force microscope and stylus profiler for surface characterisation of similar materials.

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Section: Introductionmentioning

confidence: 99%

Digital holographic microscopy of spiropyran‐based dynamic materials

Jamali,

Rad,

Razaghi

et al. 2023

Journal of Microscopy

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show abstract

“…DHM is a non-contact, non-destructive, and non-scanning technique for quantitative phase imaging, therefore, suitable for retrieving the morphology of biological samples, and surface topography of reflective ones 44 . We have already applied DHM for 3D visualization of myelin figures and their dynamics under thermal gradients 45 , changes in their environmental humidity 46 , studying microstructural surface characterization 47 , evaluation of intergranular corrosion of stainless steel 48,49 , and for 3D characterization of nanocomposites 41 .…”

Section: Introductionmentioning

confidence: 99%

Digital Holographic Microscopy of Spiropyran-Based Dynamic Materials

et al. 2022

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“…1 A significant step forward from analog holography is to record digitally the interference pattern with an electronic sensor and to reconstruct the object numerically, including the amplitude and phase information, with a computer. 2 Due to its noninvasive and label-free properties, digital holography (DH) has been applied to biological imaging, 3,4 air quality monitoring, 5 and surface characterization, 6,7 to name just a few application areas.…”

Section: Introductionmentioning

confidence: 99%

End-to-end deep learning framework for digital holographic reconstruction

2019

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Digital holography records the entire wavefront of an object, including amplitude and phase. To reconstruct the object numerically, we can backpropagate the hologram with Fresnel-Kirchhoff integralbased algorithms such as the angular spectrum method and the convolution method. Although effective, these techniques require prior knowledge, such as the object distance, the incident angle between the two beams, and the source wavelength. Undesirable zero-order and twin images have to be removed by an additional filtering operation, which is usually manual and consumes more time in off-axis configuration. In addition, for phase imaging, the phase aberration has to be compensated, and subsequently an unwrapping step is needed to recover the true object thickness. The former either requires additional hardware or strong assumptions, whereas the phase unwrapping algorithms are often sensitive to noise and distortion. Furthermore, for a multisectional object, an all-in-focus image and depth map are desired for many applications, but current approaches tend to be computationally demanding. We propose an end-to-end deep learning framework, called a holographic reconstruction network, to tackle these holographic reconstruction problems. Through this data-driven approach, we show that it is possible to reconstruct a noise-free image that does not require any prior knowledge and can handle phase imaging as well as depth map generation.

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Microstructural surface characterization of stainless and plain carbon steel using digital holographic microscopy

Cited by 23 publications

References 23 publications

Digital holographic microscopy of spiropyran‐based dynamic materials

Digital holographic microscopy of spiropyran‐based dynamic materials

Digital Holographic Microscopy of Spiropyran-Based Dynamic Materials

End-to-end deep learning framework for digital holographic reconstruction

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Microstructural surface characterization of stainless and plain carbon steel using digital holographic microscopy

Cited by 23 publications

References 23 publications

Digital holographic microscopy of spiropyran‐based dynamic materials

Digital holographic microscopy of spiropyran‐based dynamic materials

Digital Holographic Microscopy of Spiropyran-Based Dynamic Materials ​

End-to-end deep learning framework for digital holographic reconstruction

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Digital Holographic Microscopy of Spiropyran-Based Dynamic Materials