  Low-light image enhancement - a pervasive but challenging problem, plays a
central role in enhancing the visibility of an image captured in a poor
illumination environment. Due to the fact that not all photons can pass the
Bayer-Filter on the sensor of the color camera, in this work, we first present
a De-Bayer-Filter simulator based on deep neural networks to generate a
monochrome raw image from the colored raw image. Next, a fully convolutional
network is proposed to achieve the low-light image enhancement by fusing
colored raw data with synthesized monochrome raw data. Channel-wise attention
is also introduced to the fusion process to establish a complementary
interaction between features from colored and monochrome raw images. To train
the convolutional networks, we propose a dataset with monochrome and color raw
pairs named Mono-Colored Raw paired dataset (MCR) collected by using a
monochrome camera without Bayer-Filter and a color camera with Bayer-Filter.
The proposed pipeline take advantages of the fusion of the virtual monochrome
and the color raw images and our extensive experiments indicate that
significant improvement can be achieved by leveraging raw sensor data and
data-driven learning.
