@inproceedings{
li2024sirlut,
title={{SIRLUT}: Simulated Infrared Fusion Guided Image-adaptive 3D Lookup Tables for Lightweight Image Enhancement},
author={Kaijiang Li and Hao Li and Haining Li and Peisen Wang and Chunyi Guo and Wenfeng Jiang},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=8UoXBkptla}
}
Researchers have applied 3D Lookup Tables (LUTs) in cameras, offering new possibilities for enhancing image quality and achieving various tonal effects. However, these approaches often overlook the non-uniformity of color distribution in the original images, which limits the performance of learnable LUTs. To address this issue, we introduce a lightweight end-to-end image enhancement method called  Simulated Infrared Fusion Guided Image-adaptive 3D Lookup Tables (SIRLUT). SIRLUT enhances the adaptability of 3D LUTs by reorganizing the color distribution of images through the integration of simulated infrared imagery. Specifically, SIRLUT consists of an efficient Simulated Infrared Fusion (SIF) module and a Simulated Infrared Guided (SIG) refinement module. The SIF module leverages a cross-modal channel attention mechanism to perceive global information and generate dynamic 3D LUTs,  while the SIG refinement module blends simulated infrared images to match image consistency features from both structural and color aspects, achieving local feature fusion. Experimental results demonstrate that SIRLUT outperforms state-of-the-art methods on different tasks by up to 0.88 $\sim$ 2.25dB while reducing the number of parameters. Code is available at \href{https://github.com/riversky2025/SIRLUT.git}{https://github.com/riversky2025/SIRLUT}.
SIRLUT: Simulated Infrared Fusion Guided Image-adaptive 3D Lookup Tables for Lightweight Image Enhancement
Kaijiang Li Hao Li Haining Li Peisen Wang Chunyi Guo Wenfeng Jiang