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Restoring images from low-light data is a challenging problem. Most existingdeep-network based algorithms are designed to be trained with pairwise images.Due to the lack of real-world datasets, they usually perform poorly whengeneralized in practice in terms of loss of image edge and color information.In this paper, we propose an unsupervised generation network withattention-guidance to handle the low-light image enhancement task.Specifically, our network contains two parts: an edge auxiliary module thatrestores sharper edges and an attention guidance module that recovers morerealistic colors. Moreover, we propose a novel loss function to make the edgesof the generated images more visible. Experiments validate that our proposedalgorithm performs favorably against state-of-the-art methods, especially forreal-world images in terms of image clarity and noise control.

LEUGAN:Low-Light Image Enhancement by Unsupervised Generative Attentional Networks