Time Stretch

Mahjoubfar, Ata; Chen, Claire Lifan; Jalali, Bahram

doi:10.1007/978-3-319-51448-2_2

Cited by 1 publication

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…DL can extract features from multimodal dataset and recognizes the patterns 54 . This property makes it suitable for various tasks, such as classification, 55 cancer detection, 56 and segmentation 57 .…”

Section: Principlementioning

confidence: 99%

“…DL can extract features from multimodal dataset and recognizes the patterns. 54 This property makes it suitable for various tasks, such as classification, 55 cancer detection, 56 and segmentation. 57 A DL model is composed of multiple hidden layers, at every layer input is the linear combination of previous layer output, which then passes through a nonlinear function on weighted sums.…”

Section: Principlementioning

confidence: 99%

See 1 more Smart Citation

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

Thapa,

Galande,

Ram

et al. 2024

J. Biomed. Opt.

View full text Add to dashboard Cite

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 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. 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.

show abstract