  Custom and natural lighting conditions can be emulated in images of the scene
during post-editing. Extraordinary capabilities of the deep learning framework
can be utilized for such purpose. Deep image relighting allows automatic photo
enhancement by illumination-specific retouching. Most of the state-of-the-art
methods for relighting are run-time intensive and memory inefficient. In this
paper, we propose an efficient, real-time framework Deep Stacked Relighting
Network (DSRN) for image relighting by utilizing the aggregated features from
input image at different scales. Our model is very lightweight with total size
of about 42 MB and has an average inference time of about 0.0116s for image of
resolution $1024 \times 1024$ which is faster as compared to other multi-scale
models. Our solution is quite robust for translating image color temperature
from input image to target image and also performs moderately for light
gradient generation with respect to the target image. Additionally, we show
that if images illuminated from opposite directions are used as input, the
qualitative results improve over using a single input image.
