A Novel Low Light Image Enhancement Method Based on Multi-Attention Generative Adversarial Networks

Abstract

Images taken in low light realistic conditions cannot capture enough illumination to show more details, so that the recovered images show color deviation and unclear edge textures along with much noise information in the experiments of low light image enhancement. In order to obtain high brightness and clear images, this paper designs a network based on multi-attention Generative Adversarial Networks for low light image enhancement. The network uses a low illumination enhancement module that incorporates the non-local illumination attention and edge aware attention proposed in this paper. Non-local illumination attention can combine global information to quickly capture the relationship of long-range features, solving the color deviation problem while reducing the number of convolution layers. Edge aware attention focuses on local information at the edges, highlighting the edge contours of the image while smoothing out the noise. These two attentions serve as a complement to each other. And Attentional Feature Fusion is used to fuse the features of different branches and retain more valid information. It is proved that our proposed low light enhancement network is superior to other networks in terms of color fidelity, edge clarity, and denoising.