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.

本文提出了一个使用深度神经网络实现的 De-Bayer-Filter 模拟器，以生成单色原始图像，同时使用融合颜色原始数据和合成单色原始数据的完全卷积网络实现了低光图像增强。实验结果表明，通过利用原始传感器数据和数据驱动的学习，可以显著提高图像质量。