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.

翻译：近年来，许多算法采用图像自适应查找表(LUTs)来实现实时图像增强。然而，现有方法的主流趋势是通过基本LUT的线性组合来构建图像自适应LUT，这限制了这些方法的泛化能力。为解决这一局限，我们提出了名为AttentionLut的实时图像增强新框架，该框架利用注意力机制生成图像自适应LUT。所提框架由三个轻量级模块组成。首先，采用全局图像上下文特征模块提取图像自适应特征。其次，注意力融合模块将图像特征与训练过程中获得的先验注意力特征相结合，生成图像自适应规范多路张量。最后，部署规范多路重建模块重构图像自适应残差3DLUT，进而用于增强输入图像。在基准MIT-Adobe FiveK数据集上的实验证明，与现有最先进方法相比，所提方法在定量和定性上均取得了更优的增强性能。