AttentionLut: Attention Fusion-based Canonical Polyadic LUT for
  Real-time Image Enhancement
Kang Fu Yicong Peng Zicheng Zhang Qihang Xu Xiaohong Liu Jia Wang Guangtao Zhai
Recently, many algorithms have employed image-adaptive lookup tables (LUTs)
to achieve real-time image enhancement. Nonetheless, a prevailing trend among
existing methods has been the employment of linear combinations of basic LUTs
to formulate image-adaptive LUTs, which limits the generalization ability of
these methods. To address this limitation, we propose a novel framework named
AttentionLut for real-time image enhancement, which utilizes the attention
mechanism to generate image-adaptive LUTs. Our proposed framework consists of
three lightweight modules. We begin by employing the global image context
feature module to extract image-adaptive features. Subsequently, the attention
fusion module integrates the image feature with the priori attention feature
obtained during training to generate image-adaptive canonical polyadic tensors.
Finally, the canonical polyadic reconstruction module is deployed to
reconstruct image-adaptive residual 3DLUT, which is subsequently utilized for
enhancing input images. Experiments on the benchmark MIT-Adobe FiveK dataset
demonstrate that the proposed method achieves better enhancement performance
quantitatively and qualitatively than the state-of-the-art methods.