Optical coherence tomography is a valuable tool for in vivo examination thanks to its superior combination of axial resolution, field-of-view and working distance. OCT images are reconstructed from several phases that are obtained by modulation/multiplexing of light wavelength or optical path. This paper shows that only one phase (and one camera frame) is sufficient for en face tomography. The idea is to encode a high-frequency fringe patterns into the selected layer of the sample using low-coherence interferometry. These patterns can then be efficiently extracted with a high-pass filter enhanced via deep learning networks to create the tomographic full-field OCT view. This brings 10-fold improvement in imaging speed, considerably reducing the phase errors and incoherent light artifacts related to in vivo movements. Moreover, this work opens a path for low-cost tomography with slow consumer cameras. Optically, the device resembles the conventional time-domain full-field OCT without incurring additional costs or a field-of-view/resolution reduction. The approach is validated by imaging in vivo cornea in human subjects. Open-source and easy-to-follow codes for data generation/training/inference with U-Net/Pix2Pix networks are provided to be used in a variety of image-to-image translation tasks.