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

arXiv: https://arxiv.org/abs/2401.01569 · HTML: https://mlsanity.com/paper/2401.01569/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 24 references that are in the map.
- First posted on arXiv 3 Jan 2024.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [TensoRF: Tensorial Radiance Fields](https://mlsanity.com/paper/2203.09517/) (2022)
- [GridDehazeNet: Attention-Based Multi-Scale Network for Image Dehazing](https://mlsanity.com/paper/1908.03245/) (2019)
- [Deep Bilateral Learning for Real-Time Image Enhancement](https://mlsanity.com/paper/1707.02880/) (2017)
- [Learning Image-adaptive 3D Lookup Tables for High Performance Photo Enhancement in Real-time](https://mlsanity.com/paper/2009.14468/) (2020)
- [Conditional Sequential Modulation for Efficient Global Image Retouching](https://mlsanity.com/paper/2009.10390/) (2020)
- [AdaInt: Learning Adaptive Intervals for 3D Lookup Tables on Real-time Image Enhancement](https://mlsanity.com/paper/2204.13983/) (2022)
- [Real-time Image Enhancer via Learnable Spatial-aware 3D Lookup Tables](https://mlsanity.com/paper/2108.08697/) (2021)
- [No-Reference Quality Assessment for 3D Colored Point Cloud and Mesh Models](https://mlsanity.com/paper/2107.02041/) (2021)
- [SepLUT: Separable Image-adaptive Lookup Tables for Real-time Image Enhancement](https://mlsanity.com/paper/2207.08351/) (2022)
- [Subjective and Objective Quality Assessment for in-the-Wild Computer Graphics Images](https://mlsanity.com/paper/2303.08050/) (2023)
- [FastLLVE: Real-Time Low-Light Video Enhancement with Intensity-Aware Lookup Table](https://mlsanity.com/paper/2308.06749/) (2023)
- [GridDehazeNet+: An Enhanced Multi-Scale Network with Intra-Task Knowledge Transfer for Single Image Dehazing](https://mlsanity.com/paper/2103.13998/) (2021)
- [Perceptual Quality Assessment for Digital Human Heads](https://mlsanity.com/paper/2209.09489/) (2022)
- [TransMRSR: Transformer-based Self-Distilled Generative Prior for Brain MRI Super-Resolution](https://mlsanity.com/paper/2306.06669/) (2023)

## Built on by (most cited first, 11 of 11)

- [GLARE: Low Light Image Enhancement via Generative Latent Feature based Codebook Retrieval](https://mlsanity.com/paper/2407.12431/) (2024)
- [Learning Hazing to Dehazing: Towards Realistic Haze Generation for Real-World Image Dehazing](https://mlsanity.com/paper/2503.19262/) (2025)
- [Low-Light Image Enhancement via Generative Perceptual Priors](https://mlsanity.com/paper/2412.20916/) (2024)
- [ShadowRefiner: Towards Mask-free Shadow Removal via Fast Fourier Transformer](https://mlsanity.com/paper/2406.02559/) (2024)
- [ECMamba: Consolidating Selective State Space Model with Retinex Guidance for Efficient Multiple Exposure Correction](https://mlsanity.com/paper/2410.21535/) (2024)
- [DehazeDCT: Towards Effective Non-Homogeneous Dehazing via Deformable Convolutional Transformer](https://mlsanity.com/paper/2407.05169/) (2024)
- [HazeCLIP: Towards Language Guided Real-World Image Dehazing](https://mlsanity.com/paper/2407.13719/) (2024)
- [Learning Differential Pyramid Representation for Tone Mapping](https://mlsanity.com/paper/2412.01463/) (2024)
- [CameraMaster: Unified Camera Semantic-Parameter Control for Photography Retouching](https://mlsanity.com/paper/2511.21024/) (2025)
- [AU-IQA: A Benchmark Dataset for Perceptual Quality Assessment of AI-Enhanced User-Generated Content](https://mlsanity.com/paper/2508.05016/) (2025)
- [LoopLUT: 3D Lookup Tables with Progressive Region Refinement for Real-Time 4K Image Enhancement](https://mlsanity.com/paper/2609.33593/) (2026)

## Abstract (arXiv)

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.