Darkness brings us uncertainty, worry and low confidence. This is a problem not only applicable to us walking in a dark evening but also for drivers driving a car on the road with very dim or even without lighting condition. To address this problem, we propose a new CNN structure named as Video Lightening Network (VLN) that regards the low-light enhancement as a residual learning task, which is useful as reference to indirectly lightening the environment, or for vision-based application systems, such as driving assistant systems. The VLN consists of several Lightening Back-Projection (LBP) and Temporal Aggregation (TA) blocks. Each LBP block enhances the low-light frame by domain transfer learning that iteratively maps the frame between the low- and normal-light domains. A TA block handles the motion among neighboring frames by investigating the spatial and temporal relationships. Several TAs work in a multi-scale way, which compensates the motions at different levels. The proposed architecture has a consistent enhancement for different levels of illuminations, which significantly increases the visual quality even in the extremely dark environment. Extensive experimental results show that the proposed approach outperforms other methods under both objective and subjective metrics.