  Images obtained in real-world low-light conditions are not only low in
brightness, but they also suffer from many other types of degradation, such as
color bias, unknown noise, detail loss and halo artifacts. In this paper, we
propose a very fast deep learning framework called Bringing the Lightness
(denoted as BLNet) that consists of two U-Nets with a series of well-designed
loss functions to tackle all of the above degradations. Based on Retinex
Theory, the decomposition net in our model can decompose low-light images into
reflectance and illumination and remove noise in the reflectance during the
decomposition phase. We propose a Noise and Color Bias Control module (NCBC
Module) that contains a convolutional neural network and two loss functions
(noise loss and color loss). This module is only used to calculate the loss
functions during the training phase, so our method is very fast during the test
phase. This module can smooth the reflectance to achieve the purpose of noise
removal while preserving details and edge information and controlling color
bias. We propose a network that can be trained to learn the mapping between
low-light and normal-light illumination and enhance the brightness of images
taken in low-light illumination. We train and evaluate the performance of our
proposed model over the real-world Low-Light (LOL) dataset), and we also test
our model over several other frequently used datasets (LIME, DICM and MEF
datasets). We conduct extensive experiments to demonstrate that our approach
achieves a promising effect with good rubustness and generalization and
outperforms many other state-of-the-art methods qualitatively and
quantitatively. Our method achieves high speed because we use loss functions
instead of introducing additional denoisers for noise removal and color
correction. The code and model are available at
https://github.com/weixinxu666/BLNet.
