Plane-wave compounding is to sum up several successive plane waves incident at different angles to form an image. By applying time-reversal of the received signals, transmit focusing can be synthesized. Unfortunately, to improve the temporal resolution, the number of plane waves should be reduced, which often degrades the image quality. To address this problem, an image domain learning method using neural networks has been proposed, but the network needs to be retrained when the number of plane waves changes. Herein, we propose, for the first time, a universal plane-wave compounding scheme using deep learning to directly process plane waves and RF data acquired at different view angles and sub-sampling rate to generate high quality US images.