Video motion magnification makes subtle invisible motions visible, such as small chest movements while breathing, subtle vibrations in the moving objects etc. But small motions are prone to noise, illumination changes, large motions, etc. making the task difficult. Most state-of-the-art methods use hand-crafted concepts which result in small magnification, ringing artifacts etc. The deep learning based approach has higher magnification but is prone to severe artifacts in some scenarios. We propose a new phase based deep network for video motion magnification that operates in both domains (frequency and spatial) to address this issue. It generates motion magnification from frequency domain phase fluctuations and then improves its quality in the spatial domain. The proposed models are lightweight networks with fewer parameters (~ 0.11M and ~ 0.05M). Further, the proposed networks performance is compared to the SOTA approaches and evaluated on real-world and synthetic videos. Finally, an ablation study is also conducted to show the impact of different parts of the network.