MSR-net:Low-light Image Enhancement Using Deep Convolutional Network
This addresses the problem of low contrast in images for computer vision tasks, but it is incremental as it builds on Retinex theory with a machine learning approach.
The paper tackles low-light image enhancement by proposing MSR-net, a deep convolutional network that learns an end-to-end mapping from dark to bright images, achieving advantages over state-of-the-art methods in qualitative and quantitative experiments.
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.