卷积神经网络
人工智能
颜色恒定性
计算机科学
卷积(计算机科学)
深度学习
透视图(图形)
高斯分布
图像(数学)
计算机视觉
模式识别(心理学)
图像增强
核(代数)
人工神经网络
数学
组合数学
物理
量子力学
作者
Liang Shen,Zihan Yue,Fan Feng,Quan Chen,Shihao Liu,Jie Ma
出处
期刊:Cornell University - arXiv
日期:2017-11-07
被引量:30
标识
DOI:10.48550/arxiv.1711.02488
摘要
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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