AttentionLut: Attention Fusion-based Canonical Polyadic LUT for Real-time Image Enhancement

• URL: http://arxiv.org/abs/2401.01569v1
• Date: Wed, 3 Jan 2024 06:55:06 GMT
• Title: AttentionLut: Attention Fusion-based Canonical Polyadic LUT for Real-time Image Enhancement
• Authors: Kang Fu, Yicong Peng, Zicheng Zhang, Qihang Xu, Xiaohong Liu, Jia Wang, Guangtao Zhai
• Abstract summary: We propose a novel framework named AttentionLut for real-time image enhancement. Our proposed framework consists of three lightweight modules. Experiments on the benchmark MIT-Adobe FiveK dataset demonstrate that the proposed method achieves better enhancement performance than the state-of-the-art methods.
• Score: 38.61183657952919
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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