Restoring Extremely Dark Images in Real Time

A practical low-light enhancement solution must be computationally fast, memory-efficient, and achieve a visually appealing restoration. Most of the existing methods target restoration quality and thus compromise on speed and memory requirements, raising concerns about their realworld deployability. We propose a new deep learning architecture for extreme low-light single image restoration, which despite its fast & lightweight inference, produces a restoration that is perceptually at par with state-of-the-art computationally intense models. To achieve this, we do most of the processing in the higher scale-spaces, skipping the intermediate-scales wherever possible. Also unique to our model is the potential to process all the scale-spaces concurrently, offering an additional 30% speedup without compromising the restoration quality. Pre-amplification of the dark raw-image is an important step in extreme lowlight image enhancement. Most of the existing state of the art methods need GT exposure value to estimate the preamplification factor, which is not practically feasible. Thus, we propose an amplifier module that estimates the amplification factor using only the input raw image and can be used "off-the-shelf" with pre-trained models without any fine-tuning. We show that our model can restore an ultrahigh-definition 4K resolution image in just 1 sec. on a CPU and at 32 f ps on a GPU and yet maintain a competitive restoration quality. We also show that our proposed model, without any fine-tuning, generalizes well to cameras not seen during training and to subsequent tasks such as object detection.