Abstract
Enhancing RAW images captured under low light conditions is a challenging task. Recent deep learning based RAW enhancement methods have shifted from using real paired data to relying on synthetic datasets. These synthetic datasets are typically generated by physically modeling sensor noise, but existing approaches often consider only additive noise, ignore multiplicative components, and rely on global calibration that overlooks pixel level manufacturing variations. As a result, such methods struggle to accurately reproduce real sensor noise. To address these limitations, this paper derives a noise model from the physical noise generation mechanisms that occur under low illumination and proposes a novel composite model that integrates both additive and multiplicative noise. To solve the model, we introduce a physics based per pixel noise simulation and calibration scheme that estimates and synthesizes noise for each individual pixel, thereby overcoming the restrictions of traditional global calibration and capturing spatial noise variations induced by microscopic CMOS manufacturing differences. Motivated by the strong performance of rectified flow methods in image generation and processing, we further combine the physics-based noise synthesis with a rectified flow generative framework and present PGRF a physics-guided rectified flow framework for low light image enhancement. PGRF leverages the ability of rectified flows to model complex data distributions and uses physical guidance to steer the generation toward the desired clean image. To validate the effectiveness of the proposed model, we established the LLID dataset, an indoor low light benchmark captured with the Sony A7S II camera. Experimental results demonstrate that the proposed framework achieves significant improvements in low light RAW image enhancement.
Abstract (translated)
在低光条件下增强RAW图像是一项挑战性的任务。近期基于深度学习的RAW图像增强方法已从使用真实的配对数据转向依赖于合成数据集。这些合成数据集通常是通过物理建模传感器噪声来生成的，但现有的方法往往只考虑加性噪声，忽略乘法成分，并且依赖全局校准而忽略了像素级别的制造差异。因此，这样的方法难以准确再现实际传感器噪声。 为了解决这些问题，本文从低光照条件下的噪声生成机制出发推导出了一种新的噪声模型，并提出了一种集成了加性和乘法噪声的复合模型。为了求解该模型，我们引入了一个基于物理原理的逐像素噪声模拟和校准方案，它能够估计并合成每个单独像素的噪声，从而克服了传统全局校准的限制，并捕捉到由微小CMOS制造差异引起的空间噪声变化。 受到矫正流方法在图像生成和处理中表现出的强大性能的启发，我们将基于物理原理的噪声合成与一个矫正流生成框架相结合，并提出了PGRF（物理引导矫正流框架），用于低光图像增强。PGRF利用了矫正流建模复杂数据分布的能力，并采用物理指导使生成过程朝着所需的清晰图像方向发展。 为了验证所提出模型的有效性，我们建立了LLID数据集，这是一个使用索尼A7S II相机拍摄的室内低光照基准测试集。实验结果表明，所提出的框架在低光RAW图像增强方面取得了显著改进。
URL
https://arxiv.org/abs/2509.08330
