The Retinex model is one of the most representative and effective methods for low-light image enhancement. However, the Retinex model does not explicitly tackle the noise problem, and shows unsatisfactory enhancing results. In recent years, due to the excellent performance, deep learning models have been widely used in low-light image enhancement. However, these methods have two limitations: i) The desirable performance can only be achieved by deep learning when a large number of labeled data are available. However, it is not easy to curate massive low/normal-light paired data; ii) Deep learning is notoriously a black-box model [1]. It is difficult to explain their inner-working mechanism and understand their behaviors. In this paper, using a sequential Retinex decomposition strategy, we design a plug-and-play framework based on the Retinex theory for simultaneously image enhancement and noise removal. Meanwhile, we develop a convolutional neural network-based (CNN-based) denoiser into our proposed plug-and-play framework to generate a reflectance component. The final enhanced image is produced by integrating the illumination and reflectance with gamma correction. The proposed plug-and-play framework can facilitate both post hoc and ad hoc interpretability. Extensive experiments on different datasets demonstrate that our framework outcompetes the state-of-the-art methods in both image enhancement and denoising.

翻译：Retinex模型是低光照图像增强领域最具代表性和有效性的方法之一。然而，该模型未能明确处理噪声问题，导致增强效果不尽人意。近年来，深度学习模型凭借其优异性能被广泛应用于低光照图像增强。但此类方法存在两个局限性：（i）深度学习仅在具备大量标注数据时才能取得理想性能，而构建大规模低光/正常光配对数据集并不容易；（ii）深度学习被认为是一个典型的黑箱模型[1]，其内部工作机制与行为模式难以解释。本文采用序列化Retinex分解策略，基于Retinex理论设计了一个即插即用框架，可同步实现图像增强与噪声去除。同时，我们将基于卷积神经网络的去噪器（CNN-based denoiser）集成到所提出的即插即用框架中，以生成反射分量。最终通过伽马校正融合光照分量与反射分量得到增强图像。该即插即用框架可同时支持事后可解释性与预设可解释性。跨不同数据集的广泛实验表明，本框架在图像增强与去噪方面均优于现有最先进方法。