• Artefact removal in ground truth deficient fluctuations-based nanoscopy images using deep learning 

      Jadhav, Suyog; Acuña Maldonado, Sebastian Andres; Opstad, Ida Sundvor; Ahluwalia, Balpreet Singh; Agarwal, Krishna; Prasad, Dilip K. (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-12-08)
      Image denoising or artefact removal using deep learning is possible in the availability of supervised training dataset acquired in real experiments or synthesized using known noise models. Neither of the conditions can be fulfilled for nanoscopy (super-resolution optical microscopy) images that are generated from microscopy videos through statistical analysis techniques. Due to several physical ...
    • Object detection neural network improves Fourier ptychography reconstruction 

      Ströhl, Florian; Jadhav, Suyog; Ahluwalia, Balpreet Singh; Agarwal, Krishna; Prasad, Dilip K. (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-11-23)
      High resolution microscopy is heavily dependent on superb optical elements and superresolution microscopy even more so. Correcting unavoidable optical aberrations during post-processing is an elegant method to reduce the optical system’s complexity. A prime method that promises superresolution, aberration correction, and quantitative phase imaging is Fourier ptychography. This microscopy technique ...