Ms. Ashwini S. Galande Profile

Ashwini S. Galande

at Indian Institute of Technology Hyderabad

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Author

Publications (4)

SPIE Journal Paper | 8 October 2024 Open Access

High-resolution lensless holographic microscopy using a physics-aware deep network

Ashwini S. Galande, Vikas Thapa, Aswathy Vijay, Renu John

JBO, Vol. 29, Issue 10, 106502, (October 2024) https://doi.org/10.1117/12.10.1117/1.JBO.29.10.106502

KEYWORDS: Holograms, Education and training, Holography, Microscopy, Phase reconstruction, Data modeling, Wave propagation, 3D image reconstruction, Physics, Biological samples

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SignificanceLensless digital inline holographic microscopy (LDIHM) is an emerging quantitative phase imaging modality that uses advanced computational methods for phase retrieval from the interference pattern. The existing end-to-end deep networks require a large training dataset with sufficient diversity to achieve high-fidelity hologram reconstruction. To mitigate this data requirement problem, physics-aware deep networks integrate the physics of holography in the loss function to reconstruct complex objects without needing prior training. However, the data fidelity term measures the data consistency with a single low-resolution hologram without any external regularization, which results in a low performance on complex biological data.AimWe aim to mitigate the challenges with trained and physics-aware untrained deep networks separately and combine the benefits of both methods for high-resolution phase recovery from a single low-resolution hologram in LDIHM.ApproachWe propose a hybrid deep framework (HDPhysNet) using a plug-and-play method that blends the benefits of trained and untrained deep models for phase recovery in LDIHM. The high-resolution phase is generated by a pre-trained high-definition generative adversarial network (HDGAN) from a single low-resolution hologram. The generated phase is then plugged into the loss function of a physics-aware untrained deep network to regulate the complex object reconstruction process.ResultsSimulation results show that the SSIM of the proposed method is increased by 0.07 over the trained and 0.04 over the untrained deep networks. The average phase-SNR is elevated by 8.2 dB over trained deep models and 9.8 dB over untrained deep networks on the experimental biological cells (cervical cells and red blood cells).ConclusionsWe showed improved performance of the HDPhysNet against the unknown perturbation in the imaging parameters such as the propagation distance, the wavelength of the illuminating source, and the imaging sample compared with the trained network (HDGAN). LDIHM, combined with HDPhysNet, is a portable and technology-driven microscopy best suited for point-of-care cytology applications.

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SPIE Journal Paper | 30 January 2024 Open Access

TIE-GANs: single-shot quantitative phase imaging using transport of intensity equation with integration of GANs

Vikas Thapa, Ashwini Galande, Gurram Hanu Phani Ram, Renu John

JBO, Vol. 29, Issue 01, 016010, (January 2024) https://doi.org/10.1117/12.10.1117/1.JBO.29.1.016010

KEYWORDS: Education and training, Gallium nitride, Phase reconstruction, Phase imaging, Image restoration, Microscopes, Data modeling, Biomedical optics, Phase retrieval, Biological samples

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SignificanceArtificial intelligence (AI) has become a prominent technology in computational imaging over the past decade. The expeditious and label-free characteristics of quantitative phase imaging (QPI) render it a promising contender for AI investigation. Though interferometric methodologies exhibit potential efficacy, their implementation involves complex experimental platforms and computationally intensive reconstruction procedures. Hence, non-interferometric methods, such as transport of intensity equation (TIE), are preferred over interferometric methods.AimTIE method, despite its effectiveness, is tedious as it requires the acquisition of many images at varying defocus planes. The proposed methodology holds the ability to generate a phase image utilizing a single intensity image using generative adversarial networks (GANs). We present a method called TIE-GANs to overcome the multi-shot scheme of conventional TIE.ApproachThe present investigation employs the TIE as a QPI methodology, which necessitates reduced experimental and computational efforts. TIE is being used for the dataset preparation as well. The proposed method captures images from different defocus planes for training. Our approach uses an image-to-image translation technique to produce phase maps and is based on GANs. The main contribution of this work is the introduction of GANs with TIE (TIE:GANs) that can give better phase reconstruction results with shorter computation times. This is the first time the GANs is proposed for TIE phase retrieval.ResultsThe characterization of the system was carried out with microbeads of 4 μm size and structural similarity index (SSIM) for microbeads was found to be 0.98. We demonstrated the application of the proposed method with oral cells, which yielded a maximum SSIM value of 0.95. The key characteristics include mean squared error and peak-signal-to-noise ratio values of 140 and 26.42 dB for oral cells and 100 and 28.10 dB for microbeads.ConclusionsThe proposed methodology holds the ability to generate a phase image utilizing a single intensity image. Our method is feasible for digital cytology because of its reported high value of SSIM. Our approach can handle defocused images in such a way that it can take intensity image from any defocus plane within the provided range and able to generate phase map.

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Proceedings Article | 9 August 2023 Paper

Low-cost portable lens less digital holographic microscope for studying anemic RBCs

Aswathy Vijay, Ashwini Subhash Galande, Renu John

Proceedings Volume 12630, 1263016 (2023) https://doi.org/10.1117/12.2670549

KEYWORDS: 3D image reconstruction, Red blood cells, Holography, Digital holography, Holograms, Biological samples, Image restoration, Reconstruction algorithms, Phase reconstruction, Microscopes

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SPIE Journal Paper | 21 October 2020

Nanometric depth phase imaging using low-cost on-chip lensless inline holographic microscopy

Hanu Phani Ram Gurram, Ashwini Galande, Renu John

OE, Vol. 59, Issue 10, 104105, (October 2020) https://doi.org/10.1117/12.10.1117/1.OE.59.10.104105

KEYWORDS: Holograms, 3D image reconstruction, Microscopes, Reconstruction algorithms, Holography, Signal to noise ratio, Phase imaging, Microscopy, Phase retrieval, Optical engineering

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