Abstract
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 this https URL.
Abstract (translated)
基于学习的方法在低光RAW图像增强方面取得了令人鼓舞的进展，然而由于缺乏相应的数据集，在环境照度降至0.0001勒克斯等极端黑暗场景中的能力仍有待探索。为此，我们提出了一种配对到配对的数据合成流水线，能够生成精确的极低光照度范围内的RAW图像（分别为0.01-0.1勒克斯、0.001-0.01勒克斯和0.0001-0.001勒克斯），并配备高质量的sRGB参考图，组成一个大规模配对数据集，命名为“极暗视界”（See-in-the-Extremely-Dark, SIED），以评估低光RAW图像增强方法。此外，我们还提出了一种基于扩散框架的方法，该框架利用了扩散模型的生成能力和固有的去噪特性，可以从极高噪声比（SNR）的RAW输入中恢复视觉上令人愉悦的结果，在此过程中引入了一个自适应光照校正模块（AICM）和一个色彩一致性损失函数，以确保准确的曝光校正和颜色还原。在我们提出的SIED以及公开可用基准上的广泛实验表明了该方法的有效性。代码和数据集可在此网址获得：[this https URL]。
URL
https://arxiv.org/abs/2506.21132
