Ut-gan: a novel unpaired textual-attention generative adversarial network for low-light text image enhancement
2023 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP(2023)
摘要
How to balance lighting and texture details to achieve the desired visual effect remains the bottleneck of existing low-light image enhancement methods. In this paper, we propose a novel Unpaired Textual-attention Generative Adversarial N network (UT-GAN) for low-light text image enhancement task. UT-GAN first uses the Zero-DCE net for initial illumination recovery and our TAM module is proposed to translate text information into a textual attention mechanism for the overall network, emphasizing attention to the details of text regions. Moreover, the method constructs an AGM-Net module to mitigate noise effects and fine-tune the illumination. Experiments show that UT-GAN outperforms existing methods in qualitative and quantitative evaluation on the widely used the low-light datasets LOL and SID.
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关键词
low-light image,unpaired image,UT-GAN,textual attention mechanism
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