Adaptive Enhancement of Extreme Low-Light Images
This work is significant for researchers and practitioners working on low-light image enhancement, as it tackles the limitation of existing methods that assume known optimal output intensity levels, leading to more robust enhancement across diverse scenarios.
This paper addresses the problem of enhancing extremely low-light images where existing methods fail due to assumptions about known optimal output intensity levels. The authors created a new dataset of 1500 raw low-light images and developed a deep learning model that can enhance images with a wide range of intensity levels at runtime, including unseen ones.
Existing methods for enhancing dark images captured in a very low-light environment assume that the intensity level of the optimal output image is known and already included in the training set. However, this assumption often does not hold, leading to output images that contain visual imperfections such as dark regions or low contrast. To facilitate the training and evaluation of adaptive models that can overcome this limitation, we have created a dataset of 1500 raw images taken in both indoor and outdoor low-light conditions. Based on our dataset, we introduce a deep learning model capable of enhancing input images with a wide range of intensity levels at runtime, including ones that are not seen during training. Our experimental results demonstrate that our proposed dataset combined with our model can consistently and effectively enhance images across a wide range of diverse and challenging scenarios.