ArticleJournal of biomedical optics2024
TIE-GANs: single-shot quantitative phase imaging using transport of intensity equation with integration of GANs.
Article in Journal of biomedical optics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Deep Learning Integration in Optical Microscopy: Advancements and Applications.Microscopy research and technique · 2026Review
- Single capture quantitative oblique back-illumination microscopy.Npj imaging · 2026Article
- Resolution-enhanced transport of intensity phase imaging using contrast transfer function reformulation and SNR-guided deconvolution.Biomedical optics express · 2026Article
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4 authors.
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Abstract
Significance: Artificial 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. Aim: TIE 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. Approach: The 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. Results: The characterization of the system was carried out with microbeads of Conclusions: The 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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