Deep-Learning Projector for Optical Diffraction Tomography
F. Yang, T.-a. Pham, H. Gupta, M. Unser, J. Ma
Optics Express, vol. 28, no. 3, pp. 3905–3921, February 3, 2020.
Optical diffraction tomography is an effective tool to estimate the refractive indices of unknown objects. It proceeds by solving an ill-posed inverse problem for which the wave equation governs the scattering events. The solution has traditionally been derived by the minimization of an objective function in which the data-fidelity term encourages measurement consistency while the regularization term enforces prior constraints. In this work, we propose to train a convolutional neural network (CNN) as the projector in a projected-gradient-descent method. We iteratively produce high-quality estimates and ensure measurement consistency, thus keeping the best of CNN-based and regularization-based worlds. Our experiments on two-dimensional-simulated and real data show an improvement over other conventional or deep-learning-based methods. Furthermore, our trained CNN projector is general enough to accommodate various forward models for the handling of multiple-scattering events.
@ARTICLE(http://bigwww.epfl.ch/publications/yang2001.html, AUTHOR="Yang, F. and Pham, T.-a. and Gupta, H. and Unser, M. and Ma, J.", TITLE="Deep-Learning Projector for Optical Diffraction Tomography", JOURNAL="Optics Express", YEAR="2020", volume="28", number="3", pages="3905--3921", month="February 3,", note="")