With the development of image recognition technology, face, body shape, and other factors have been widely used as identification labels, which provide a lot of convenience for our daily life. However, image recognition has much higher requirements for image conditions than traditional identification methods like a password. Therefore, image enhancement plays an important role in the process of image analysis for images with noise, among which the image of low-light is the top priority of our research. In this paper, a low-light image enhancement method based on the enhanced network module optimized Generative Adversarial Networks(GAN) is proposed. The proposed method first applied the enhancement network to input the image into the generator to generate a similar image in the new space, Then constructed a loss function and minimized it to train the discriminator, which is used to compare the image generated by the generator with the real image. We implemented the proposed method on two image datasets (DPED, LOL), and compared it with both the traditional image enhancement method and the deep learning approach. Experiments showed that our proposed network enhanced images have higher PNSR and SSIM, the overall perception of relatively good quality, demonstrating the effectiveness of the method in the aspect of low illumination image enhancement.

随着图像识别技术的发展，面部，身体形状等因素已被广泛用作识别标签，为我们的日常生活提供了很多便利。但是，图像识别对图像条件的要求比传统的识别方法（例如密码）高得多。因此，图像增强在噪声图像的图像分析过程中起着重要作用，其中弱光图像是我们研究的重中之重。本文提出了一种基于增强网络模块优化的Generative Adversarial Networks（GAN）的弱光图像增强方法。提出的方法首先应用增强网络将图像输入到生成器中，以在新空间中生成相似图像，然后构造一个损失函数并将其最小化以训练鉴别器，该鉴别器用于将生成器生成的图像与真实图像进行比较。我们在两个图像数据集（DPED，LOL）上实现了该方法，并将其与传统图像增强方法和深度学习方法进行了比较。实验表明，我们提出的网络增强图像具有较高的PNSR和SSIM，总体感觉质量较好，证明了该方法在低照度图像增强方面的有效性。