提出极端暗光图像增强新方法与数据集，解决0.0001 lux级极暗场景还原难题。
Learning to See in the Extremely Dark
- 构建三档极暗光条件下的配对数据合成流程，生成真实感低光RAW与sRGB图像。
- 在0.0001-0.001 lux范围内实现视觉可接受的高保真图像重建，信噪比显著提升。
- 适合低光成像、夜视系统、自动驾驶等极端光照场景研究者使用。
基于学习的方法在低光RAW图像增强中取得进展，但针对环境照度低至0.0001 lux的极端暗光场景，因缺乏对应数据集而研究有限。为此，本文提出一种配对数据合成管道，可在0.01-0.1 lux、0.001-0.01 lux、0.0001-0.001 lux三个精确照度范围生成校准良好的极低光RAW图像，并配套高质量sRGB参考图像，构建大规模配对数据集See-in-the-Extremely-Dark（SIED），用于基准测试低光RAW图像增强方法。此外，提出一种基于扩散模型的框架，利用其生成能力与固有去噪特性，从极低信噪比（SNR）RAW输入中恢复视觉愉悦的结果；引入自适应光照校正模块（AICM）和颜色一致性损失，确保准确曝光与色彩还原。在所提SIED及公开基准上的大量实验验证了方法的有效性。代码与数据集开源于https://github.com/JianghaiSCU/SIED。
原文摘要 · Abstract (English)
Learning-based methods have made promising advances in low-light RAW image enhancement, while their capability to extremely dark scenes where the environmental illuminance drops as low as 0.0001 lux remains to be explored due to the lack of corresponding datasets. To this end, we propose a paired-to-paired data synthesis pipeline capable of generating well-calibrated extremely low-light RAW images at three precise illuminance ranges of 0.01-0.1 lux, 0.001-0.01 lux, and 0.0001-0.001 lux, together with high-quality sRGB references to comprise a large-scale paired dataset named See-in-the-Extremely-Dark (SIED) to benchmark low-light RAW image enhancement approaches. Furthermore, we propose a diffusion-based framework that leverages the generative ability and intrinsic denoising property of diffusion models to restore visually pleasing results from extremely low-SNR RAW inputs, in which an Adaptive Illumination Correction Module (AICM) and a color consistency loss are introduced to ensure accurate exposure correction and color restoration. Extensive experiments on the proposed SIED and publicly available benchmarks demonstrate the effectiveness of our method. The code and dataset are available at https://github.com/JianghaiSCU/SIED.
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