Novel defocus hologram aberration compensation method in digital holography integrated pathology microscope for label free 3-D imaging

Goud, B. Karthik; Raju, S.D.V.S. Jagannadha; Rao, K. Divakar

doi:10.1016/j.optlaseng.2020.106514

Cited by 5 publications

(2 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is typically computed in various techniques such as for surface profiling measurements in interferometry and optical metrology, surface topography measurements in synthetic aperture radar, magnetic field measurements in MRI etc [1][2][3]. Phase is the main parameter of interest in Quantitative Phase Imaging techniques like Digital Holographic Microscopy [4]. Generally the calculated phase is wrapped in the range [−π, π] due to inverse trigonometrical functions involved in the computation process.…”

Section: Introductionmentioning

confidence: 99%

“…A custom built Digital Holographic Microscope (DHM) shown in figure 6(g), was used to image human epithelial cheek cells to generate experimental wrapped phase. This DHM is custom built using a pathology microscope as described by Goud et al [4] and it uses reference conjugated hologram method [38] for computation of phase maps from interference pattern. Epithelial cheek cell smear from buccal cavity of a healthy individual was prepared on a glass slide.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Transformer based deep learning hybrid architecture for phase unwrapping

Bujagouni,

Pradhan

2024

Phys. Scr.

View full text Add to dashboard Cite

A deep learning Hybrid architecture for phase unwrapping has been proposed. The hybrid architecture is based on integration of Convolutional Neural Networks (CNN) with Vision Transformer. The performance of Hybrid architecture/network in phase unwrapping is compared against CNN based standard UNET network. Structural Similarity Index (SSIM) and Root Mean Square Error (RMSE) have been used as performance metrics to assess the performance of these deep learning networks for phase unwrapping. To train and test the networks, dataset with high mean Entropy has been generated using Gaussian filtering of random noise in Fourier plane. The Hybrid architecture is tested on test dataset and is found to have superior performance metrics against the UNET network. Their performance is also tested in noisy environment with various noise levels and Hybrid architecture demonstrated better anti-noise capability than UNET network. Hybrid architecture was successfully validated in real world scenario using experimental data from custom built Digital Holographic Microscope. With the advent of newer architectures and hardware, Deep learning networks can further improve the performance in solving inverse problems.

show abstract

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

Transformer based deep learning hybrid architecture for phase unwrapping

Bujagouni,

Pradhan

2024

Phys. Scr.

View full text Add to dashboard Cite

show abstract

Compensation of aberrations in holographic microscopes: main strategies and applications

et al. 2022

View full text Add to dashboard Cite

Digital holography is a technique that provides a non-invasive, label-free, quantitative, and high-resolution imaging employable in biological and science of matter fields, but not only. In the last decade, digital holography (DH) has undergone very significant signs of progress that made it one of the most powerful metrology tools. However, one of the most important issues to be afforded and solved for obtaining quantitative phase information about the analyzed specimen is related to phase aberrations. Sources of aberrations can be diverse, and several strategies have been developed and tested to make DH a reliable optical system with submicron resolution. This paper reviews the most effective and robust methods to remove or compensate phase aberrations in retrieved quantitative phase imaging by DH. Different strategies are presented and discussed in detail on how to remove or compensate for such disturbing aberrations. Among the various methods improvements in the optical setups are considered the numerical algorithms, the hybrid methods, and the very recent Artificial Intelligence (AI) approaches to compensate for all aberrations which affect the setups to improve the imaging quality and the accuracy of the reconstruction images’ procedures.

show abstract

Fast aberration compensation based on sequential shift and differential–integral based algorithm in digital holographic microscopy

Dou

Yang

et al. 2022

Optics Communications

View full text Add to dashboard Cite

Novel defocus hologram aberration compensation method in digital holography integrated pathology microscope for label free 3-D imaging

Cited by 5 publications

References 29 publications

Transformer based deep learning hybrid architecture for phase unwrapping

Transformer based deep learning hybrid architecture for phase unwrapping

Compensation of aberrations in holographic microscopes: main strategies and applications

Fast aberration compensation based on sequential shift and differential–integral based algorithm in digital holographic microscopy

Contact Info

Product

Resources

About