Abstract
Deep neural networks (DNNs) have recently become the leading method for low-light image enhancement (LLIE). However, despite significant progress, their outputs may still exhibit issues such as amplified noise, incorrect white balance, or unnatural enhancements when deployed in real world applications. A key challenge is the lack of diverse, large scale training data that captures the complexities of low-light conditions and imaging pipelines. In this paper, we propose a novel image signal processing (ISP) driven data synthesis pipeline that addresses these challenges by generating unlimited paired training data. Specifically, our pipeline begins with easily collected high-quality normal-light images, which are first unprocessed into the RAW format using a reverse ISP. We then synthesize low-light degradations directly in the RAW domain. The resulting data is subsequently processed through a series of ISP stages, including white balance adjustment, color space conversion, tone mapping, and gamma correction, with controlled variations introduced at each stage. This broadens the degradation space and enhances the diversity of the training data, enabling the generated data to capture a wide range of degradations and the complexities inherent in the ISP pipeline. To demonstrate the effectiveness of our synthetic pipeline, we conduct extensive experiments using a vanilla UNet model consisting solely of convolutional layers, group normalization, GeLU activation, and convolutional block attention modules (CBAMs). Extensive testing across multiple datasets reveals that the vanilla UNet model trained with our data synthesis pipeline delivers high fidelity, visually appealing enhancement results, surpassing state-of-the-art (SOTA) methods both quantitatively and qualitatively.
Abstract (translated)
最近，深度神经网络（DNN）已成为低光图像增强（LLIE）的领先方法。然而，尽管取得了显著进展，它们在实际应用中的输出仍然可能出现诸如放大噪声、错误白平衡或不自然增强等问题。关键挑战之一是缺乏能够捕捉低光条件和成像流程复杂性的多样化大规模训练数据。 为此，本文提出了一种新颖的基于图像信号处理（ISP）的数据合成管道，通过生成无限制配对训练数据来解决这些问题。具体来说，我们的管道从易于收集的高质量正常光照图像开始，并使用反向ISP首先将其未加工为RAW格式。然后，在RAW域直接合成低光退化情况。随后，生成的数据会经过一系列ISP阶段处理，包括白平衡调整、颜色空间转换、色调映射和伽马校正等，同时在每个阶段引入受控变化。这拓宽了降级范围，并增强了训练数据的多样性，使得生成的数据能够捕捉到广泛的降级情况以及ISP流程中的固有复杂性。 为了证明我们合成管道的有效性，我们在一个简单的UNet模型上进行了大量实验，该模型仅由卷积层、组归一化、GeLU激活和卷积块注意模块（CBAMs）组成。在多个数据集上的广泛测试表明，使用我们的数据合成管道训练的简单UNet模型能够提供高保真度且视觉效果良好的增强结果，在量化和定性评估中均超越了最先进的方法（SOTA）。
URL
https://arxiv.org/abs/2504.12204
