Recently, Retinex theory based low-light image enhancement (LLIE) algorithms have achieved impressive results in controlled environment. However, the majority of deep learning based LLIE algorithms leverage relighting by enhancing the illumination components that directly determines the image brightness, regretfully, they ignore the information of reflectance components, which may cause problems such as image noise and color distortion in reconstructed images. To tackle this problem, in this letter, we propose a component enhancement network based on Generative Adversarial Network (Component-GAN) for recovering clear images from low-light ones. Specifically, the network is composed of the decomposition part for dividing the paired low/normal-light images into illumination components and reflectance components, and the enhancement part for generating high-quality images. It is worth to note that we provide two branches of component enhancement network, which are parallel to improve the two components simultaneously. Hereby, we treat the reconstruction part as the generative network and adopt discriminative network to boost image reconstruction performance. Through extensive experiments, the proposed approach outperforms some state-of-the-art LLIE methods in terms of visual and subjective qualities.

中文翻译：

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通过 Component-GAN 在黑暗中看到

最近，基于 Retinex 理论的低光图像增强 (LLIE) 算法在受控环境中取得了令人瞩目的成果。然而，大多数基于深度学习的 LLIE 算法通过增强直接决定图像亮度的照明分量来利用重新照明，遗憾的是，它们忽略了反射分量的信息，这可能会导致重建图像中的图像噪声和颜色失真等问题。为了解决这个问题，在这封信中，我们提出了一种基于生成对抗网络（Component-GAN）的组件增强网络，用于从低光图像中恢复清晰的图像。具体来说，网络由分解部分组成，用于将配对的低/正常光图像分为照明分量和反射分量，以及生成高质量图像的增强部分。值得注意的是，我们提供了组件增强网络的两个分支，它们并行以同时改进两个组件。因此，我们将重建部分视为生成网络，并采用判别网络来提高图像重建性能。通过大量实验，所提出的方法在视觉和主观质量方面优于一些最先进的 LLIE 方法。