Two difficulties here make low-light image enhancement a challenging task; firstly, it needs to consider not only luminance restoration but also image contrast, image denoising and color distortion issues simultaneously. Second, the effectiveness of existing low-light enhancement methods depends on paired or unpaired training data with poor generalization performance. To solve these difficult problems, we propose in this paper a new learning-based Retinex decomposition of zero-shot low-light enhancement method, called ZERRINNet. To this end, we first designed the N-Net network, together with the noise loss term, to be used for denoising the original low-light image by estimating the noise of the low-light image. Moreover, RI-Net is used to estimate the reflection component and illumination component, and in order to solve the color distortion and contrast, we use the texture loss term and segmented smoothing loss to constrain the reflection component and illumination component. Finally, our method is a zero-reference enhancement method that is not affected by the training data of paired and unpaired datasets, so our generalization performance is greatly improved, and in the paper, we have effectively validated it with a homemade real-life low-light dataset and additionally with advanced vision tasks, such as face detection, target recognition, and instance segmentation. We conducted comparative experiments on a large number of public datasets and the results show that the performance of our method is competitive compared to the current state-of-the-art methods. The code is available at:https://github.com/liwenchao0615/ZERRINNet

翻译：低光照图像增强面临两个难题：首先，它不仅需要考虑亮度恢复，还需同时处理图像对比度、图像去噪和色彩失真问题。其次，现有低光照增强方法的有效性依赖于配对或非配对训练数据，导致泛化性能较差。为解决这些难题，本文提出一种基于Retinex分解的零样本低光照增强新方法，命名为ZERRINNet。为此，我们首先设计了N-Net网络，结合噪声损失项，通过估计低光照图像的噪声来对原始低光照图像进行去噪。此外，RI-Net用于估计反射分量和照度分量，为解决色彩失真和对比度问题，我们使用纹理损失项和分段平滑损失约束反射分量和照度分量。最后，本方法为零参考增强方法，不受配对和非配对数据集训练数据的影响，因此泛化性能显著提升。我们通过自制的真实低光照数据集以及高级视觉任务（如人脸检测、目标识别和实例分割）进行了有效验证。在大量公开数据集上的对比实验表明，本方法性能与当前最先进方法相比具有竞争力。代码地址：https://github.com/liwenchao0615/ZERRINNet