Deep Phase Decoder: Self-calibrating phase microscopy with an untrained deep neural network

Deep neural networks have emerged as effective tools for computational imaging, including quantitative phase microscopy of transparent samples. To reconstruct phase from intensity, current approaches rely on supervised learning with training examples; consequently, their performance is sensitive to a match of training and imaging settings. Here we propose a new approach to phase microscopy by using an untrained deep neural network for measurement formation, encapsulating the image prior and the system physics. Our approach does not require any training data and simultaneously reconstructs the phase and pupil-plane aberrations by fitting the weights of the network to the captured images. To demonstrate experimentally, we reconstruct quantitative phase from through-focus intensity images without knowledge of the aberrations.