Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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GAN-Based Local Lightness-Aware Enhancement Network for Underexposed Images

  • Chen, Yong (School of Automation, Chongqing University of Posts and Telecommunications) ;
  • Huang, Meiyong (School of Automation, Chongqing University of Posts and Telecommunications) ;
  • Liu, Huanlin (School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications) ;
  • Zhang, Jinliang (School of Automation, Chongqing University of Posts and Telecommunications) ;
  • Shao, Kaixin (School of Automation, Chongqing University of Posts and Telecommunications)
  • Received : 2022.01.27
  • Accepted : 2022.07.21
  • Published : 2022.08.31
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Abstract

Uneven light in real-world causes visual degradation for underexposed regions. For these regions, insufficient consideration during enhancement procedure will result in over-/under-exposure, loss of details and color distortion. Confronting such challenges, an unsupervised low-light image enhancement network is proposed in this paper based on the guidance of the unpaired low-/normal-light images. The key components in our network include super-resolution module (SRM), a GAN-based low-light image enhancement network (LLIEN), and denoising-scaling module (DSM). The SRM improves the resolution of the low-light input images before illumination enhancement. Such design philosophy improves the effectiveness of texture details preservation by operating in high-resolution space. Subsequently, local lightness attention module in LLIEN effectively distinguishes unevenly illuminated areas and puts emphasis on low-light areas, ensuring the spatial consistency of illumination for locally underexposed images. Then, multiple discriminators, i.e., global discriminator, local region discriminator, and color discriminator performs assessment from different perspectives to avoid over-/under-exposure and color distortion, which guides the network to generate images that in line with human aesthetic perception. Finally, the DSM performs noise removal and obtains high-quality enhanced images. Both qualitative and quantitative experiments demonstrate that our approach achieves favorable results, which indicates its superior capacity on illumination and texture details restoration.

Keywords

Acknowledgement

The research was funded by the Chongqing Education Committee Science Foundation of China (No. KJ130529).

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