Images captured in low-light conditions usually suffer from very low
contrast, which increases the difficulty of subsequent computer vision tasks in
a great extent. In this paper, a low-light image enhancement model based on
convolutional neural network and Retinex theory is proposed. Firstly, we show
that multi-scale Retinex is equivalent to a feedforward convolutional neural
network with different Gaussian convolution kernels. Motivated by this fact, we
consider a Convolutional Neural Network(MSR-net) that directly learns an
end-to-end mapping between dark and bright images. Different fundamentally from
existing approaches, low-light image enhancement in this paper is regarded as a
machine learning problem. In this model, most of the parameters are optimized
by back-propagation, while the parameters of traditional models depend on the
artificial setting. Experiments on a number of challenging images reveal the
advantages of our method in comparison with other state-of-the-art methods from
the qualitative and quantitative perspective.

利用卷积神经网络和 Retinex 理论，基于低光照条件下图像对比度很低的事实，提出了一个低光照图像增强模型。通过实验，证明该模型在质量和量化方面比其他同类模型有优势。