  We present a data-driven approach to compensate for optical aberration in
calibration-free quantitative phase imaging (QPI). Unlike existing methods that
require additional measurements or a background region to correct aberrations,
we exploit deep learning techniques to model the physics of aberration in an
imaging system. We demonstrate the generation of a single-shot
aberration-corrected field image by using a U-net-based deep neural network
that learns a translation between an optical field with aberrations and an
aberration-corrected field. The high fidelity of our method is demonstrated on
2D and 3D QPI measurements of various confluent eukaryotic cells, benchmarking
against the conventional method using background subtractions.
