  The ability to capture good quality images in the dark and near-zero lux
conditions has been a long-standing pursuit of the computer vision community.
The seminal work by Chen et al. [5] has especially caused renewed interest in
this area, resulting in methods that build on top of their work in a bid to
improve the reconstruction. However, for practical utility and deployment of
low-light enhancement algorithms on edge devices such as embedded systems,
surveillance cameras, autonomous robots and smartphones, the solution must
respect additional constraints such as limited GPU memory and processing power.
With this in mind, we propose a deep neural network architecture that aims to
strike a balance between the network latency, memory utilization, model
parameters, and reconstruction quality. The key idea is to forbid computations
in the High-Resolution (HR) space and limit them to a Low-Resolution (LR)
space. However, doing the bulk of computations in the LR space causes artifacts
in the restored image. We thus propose Pack and UnPack operations, which allow
us to effectively transit between the HR and LR spaces without incurring much
artifacts in the restored image. We show that we can enhance a full resolution,
2848 x 4256, extremely dark single-image in the ballpark of 3 seconds even on a
CPU. We achieve this with 2 - 7x fewer model parameters, 2 - 3x lower memory
utilization, 5 - 20x speed up and yet maintain a competitive image
reconstruction quality compared to the state-of-the-art algorithms.
