Blar i forfatter "Kumar, Anand"

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    • Characterization of color cross-talk of CCD detectors and its influence in multispectral quantitative phase imaging 

      Ahmad, Azeem; Kumar, Anand; Dubey, Vishesh Kumar; Butola, Ankit; Ahluwalia, Balpreet Singh; Mehta, Dalip Singh (Journal article; Tidsskriftartikkel; Peer reviewed, 2019-02-08)
      Multi-spectral quantitative phase imaging (QPI) is an emerging imaging modality for wavelength dependent studies of several biological and industrial specimens. Simultaneous multi-spectral QPI is generally performed with color CCD cameras. Here, we present a new approach for accurately measuring the color crosstalk of 2D area detectors, without needing prior information about camera specifications. ...

    • High space-bandwidth in quantitative phase imaging using partially spatially coherent digital holographic microscopy and a deep neural network 

      Butola, Ankit; Kanade, Sheetal Raosaheb; Bhatt, Sunil; Dubey, Vishesh Kumar; Kumar, Anand; Ahmad, Azeem; Prasad, Dilip K.; Senthilkumaran, Paramasivam; Ahluwalia, Balpreet Singh; Mehta, Dalip Singh (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-11-16)
      Quantitative phase microscopy (QPM) is a label-free technique that enables monitoring of morphological changes at the subcellular level. The performance of the QPM system in terms of spatial sensitivity and resolution depends on the coherence properties of the light source and the numerical aperture (NA) of objective lenses. Here, we propose high space-bandwidth quantitative phase imaging using ...

    • Single-shot multispectral quantitative phase imaging of biological samples using deep learning 

      Bhatt, Sunil; Butola, Ankit; Kumar, Anand; Thapa, Pramila; Joshi, Akshay; Jadhav, Suyog S.; Singh, Neetu; Prasad, Dilip K.; Agarwal, Krishna; Mehta, Dalip Singh (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-05-16)
      Multispectral quantitative phase imaging (MS-QPI) is a high-contrast label-free technique for morphological imaging of the specimens. The aim of the present study is to extract spectral dependent quantitative information in single-shot using a highly spatially sensitive digital holographic microscope assisted by a deep neural network. There are three different wavelengths used in our method: 𝜆=532 , ...