High-quality images are difficult to obtain in complex environments, such as underground or underwater. The low performance of images that are captured under low-light conditions significantly restricts the development of various engineering applications. However, existing algorithms exhibit color distortion or under/overexposure when addressing non-uniform illumination images. Furthermore, they introduce high-level noise when processing extremely dark images. In this paper, we propose a novel generative adversarial network (GAN) structure to generate high-quality enhanced images, which is called anti-attention block (AAB)-based generative adversarial networks (AABGAN). Specifically, we propose AAB to suppress undesired chromatic aberrations and establish a mapping relationship between different channels. The deep aggregation pyramid pooling module guides the network when combining multi-scale context information. Furthermore, we design a new multiple loss function to adjust images to the most suitable range for human vision. The results of extensive experiments show that our method outperforms state-of-the-art unsupervised image enhancement methods in terms of noise reduction and has a well-perceived result.
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