Phase unwrapping is a seminal imaging problem encountered in numerous applications, such as interferometric synthetic aperture radar, magnetic resonance imaging, digital holography microscopy (DHM), or optical diffraction tomography (ODT). In all these applications, the measurements of the true phase values lie between 0 and 2π (i.e., the true phase values are observed modulo 2π). Phase unwrapping consists in recovering the original phase from these wrapped and possibly noisy measurements. In DHM / ODT, optically thick samples yield 2D images difficult to unwrap. Conventional techniques can fail to recover the true phase because of steep gradient. In this project, the student will implement a convolutional neural network to solve the phase unwrapping problem in DHM / ODT using Python (TensorFlow / PyTorch), and compare its performance to other existing approaches.