  Low-light images suffer from severe noise and low illumination. Current deep
learning models that are trained with real-world images have excellent noise
reduction, but a ratio parameter must be chosen manually to complete the
enhancement pipeline. In this work, we propose an adaptive low-light raw image
enhancement network to avoid parameter-handcrafting and to improve image
quality. The proposed method can be divided into two sub-models: Brightness
Prediction (BP) and Exposure Shifting (ES). The former is designed to control
the brightness of the resulting image by estimating a guideline exposure time
$t_1$. The latter learns to approximate an exposure-shifting operator $ES$,
converting a low-light image with real exposure time $t_0$ to a noise-free
image with guideline exposure time $t_1$. Additionally, structural similarity
(SSIM) loss and Image Enhancement Vector (IEV) are introduced to promote image
quality, and a new Campus Image Dataset (CID) is proposed to overcome the
limitations of the existing datasets and to supervise the training of the
proposed model. Using the proposed model, we can achieve high-quality low-light
image enhancement from a single raw image. In quantitative tests, it is shown
that the proposed method has the lowest Noise Level Estimation (NLE) score
compared with the state-of-the-art low-light algorithms, suggesting a superior
denoising performance. Furthermore, those tests illustrate that the proposed
method is able to adaptively control the global image brightness according to
the content of the image scene. Lastly, the potential application in video
processing is briefly discussed.
