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arXiv · 1711.02488

MSR-net:Low-light Image Enhancement Using Deep Convolutional Network

Liang Shen·Zihan Yue·Fan Feng·Quan Chen·Shihao Liu·Jie Ma
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Abstract

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.

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Liang Shen, Zihan Yue, Fan Feng, Quan Chen, Shihao Liu, Jie Ma. 2017-11-07. MSR-net:Low-light Image Enhancement Using Deep Convolutional Network. https://arxiv.org/abs/1711.02488

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