Browsing by Author "Shanmugavel, Sibi Chakravarthy"

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    High Resolution Phase Imaging using Transport of Intensity Equation
    Shanmugavel, Sibi Chakravarthy (Virginia Tech, 2021-06-23)
    Quantitative phase Imaging(QPI) has emerged as a valuable tool for imaging specimens with weak scattering and absorbing abilities such as cells and tissues. It is complementary to fluorescence microscopy, as such, it can be applied to unlabelled specimens without the need for fluorescent tagging. By quantitatively mapping the phase changes induced in the incident light field by the optical path length delays of the specimen, QPI provides objective measurement of the cellular dynamics and enables imaging the specimen with high contrast. Transport of Intensity Equation(TIE) is a powerful computational tool for QPI because of its experimental and computational simplicity. Using TIE, the phase can be quantitatively retrieved from defocused intensity images. However, the resolution of the phase image computed using TIE is limited by the diffraction limit of the imaging system used to capture the intensity images. In this thesis, we have developed a super resolution phase imaging technique by applying the principles of Structured Illumination Microscopy(SIM) to Transport of Intensity phase retrieval. The modulation from the illumination shifts the high frequency components of the phase object into the system pass-band. This enables phase imaging with resolutions exceeding the diffraction limit. The proposed method is experimentally validated using a custom-made upright microscope. Because of its experimental and computational simplicity, the method in this thesis should find application in biomedical laboratories where super resolution phase imaging is required
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    Physics-informed neural network for phase imaging based on transport of intensity equation
    Wu, Xiaofeng; Wu, Ziling; Shanmugavel, Sibi Chakravarthy; Yu, Hang Z.; Zhu, Yunhui (Optica Publishing Group, 2022-11)
    Non-interferometric quantitative phase imaging based on Transport of Intensity Equation (T1E) has been widely used in bio-medical imaging. However, analytic TIE phase retrieval is prone to low-spatial frequency noise amplification, which is caused by the iliposedness of inversion at the origin of the spectrum. There are also retrieval ambiguities resulting from the lack of sensitivity to the curl component of the Poynting vector occurring with strong absorption. Here, we establish a physics-informed neural network (PINN) to address these issues, by integrating the forward and inverse physics models into a cascaded deep neural network. We demonstrate that the proposed PINN is efficiently trained using a small set of sample data, enabling the conversion of noise-corrupted 2-shot TIE phase retrievals to high quality phase images under partially coherent LED illumination. The efficacy of the proposed approach is demonstrated by both simulation using a standard image database and experiment using human buccal epitehlial cells. In particular, high image quality (SSIM = 0.919) is achieved experimentally using a reduced size of labeled data (140 image pairs). We discuss the robustness of the proposed approach against insufficient training data, and demonstrate that the parallel architecture of PINN is efficient for transfer learning.