目的 在日常的图像采集工作中，由于场景光照条件差或设备的补光能力不足，容易产生低照度图像。为了解决低照度图像视觉感受差、信噪比低和使用价值低（难以分辨图像内容）等问题，本文提出一种基于条件生成对抗网络的低照度图像增强方法。方法 本文设计一个具备编解码功能的卷积神经网络（CNN）模型作为生成模型，同时加入具备二分类功能的CNN作为判别模型，组成生成对抗网络。在模型训练的过程中，以真实的亮图像为条件，依靠判别模型监督生成模型以及结合判别模型与生成模型间的相互博弈，使得本文网络模型具备更好的低照度图像增强能力。在本文方法使用过程中，无需人工调节参数，图像输入模型后端到端处理并输出结果。结果 将本文方法与现有方法进行比较，利用本文方法增强的图像在亮度、清晰度以及颜色还原度等方面有了较大的提升。在峰值信噪比、直方图相似度和结构相似性等图像质量评价指标方面，本文方法比其他方法的最优值分别提高了0.7 dB、3.9%和8.2%。在处理时间上，本文方法处理图像的速度远远超过现有的传统方法，可达到实时增强的要求。结论 通过实验比较了本文方法与现有方法对于低照度图像的处理效果，表明本文方法具有更优的处理效果，同时具有更快的处理速度。

图像处理; 低照度图像增强; 卷积神经网络; 条件生成对抗网络; 深度学习

Objective Low-illumination images are easily produced when taking pictures because of weak lighting conditions or devices with poor filling flash. Low-illumination images are difficult to recognize. Thus, the quality of low-illumination images needs to be improved. In the past, low-illumination image enhancement was dominated by histogram equalization (HE) and Retinex, but these methods cannot easily generate the desired results. Their results often entail problems, such as color distortion and blurred edges. A conditional generative adversarial network (CGAN)-based method is proposed to solve this poor visual perception problem. CGAN is an extension of the generative adversarial network (GAN). At present, it is widely used in data generation, including image de-raining, image resolution enhancement, and speech denoising. Unlike traditional low-illumination image enhancement methods that work on single image adjustment, this method achieves data-driven enhancement. Method This study proposes an encode-decode convolutional neural network (CNN) model as the generative model and a CNN model with a classification function as the discriminative model. The two models constitute a GAN. The model processes input images end to end and without adjusting the parameters manually. Instead of using synthetic image datasets, real-shot low-illumination images from the multi-exposure image dataset are used for training and testing. This image dataset contains multi-exposure sequential images, including under-and over-exposure images. The exposure of images is shifted by the exposure value (EV) of cameras or phones. Moreover, this dataset offers high-quality reference light images. During training, by offering reference light images from datasets as conditions to GAN, both models optimize their parameters according to the light images. As a result, the entire model is transformed into CGAN. The coding path of the generative model samples low-illumination images and processes the images at different scales. After coding, the encoding path restores the image size and shortens the distance between the outputs and conditional light images. The low-illumination images are denoised and restored by a different convolution processing of the generative model, and enhanced images are obtained. The discriminative model distinguishes the enhanced and reference light images by comparing their differences. The enhanced images are regarded as false, and the reference light images are regarded as true. Then, the discriminative model provides the result to the generative model. According to the feedback, the generative model optimizes the parameters to obtain an improved enhancement capability, and the discriminative model obtains an improved distinguishing capability by optimizing its own parameters. After training thousands of pairs of images, the parameters of both models are optimized. By using the discriminative model to supervise the generative model and by combining the interrelation between the two models, an improved image enhancement effect is achieved. When the proposed model is used to enhance low-illumination images, the discriminative model is no longer involved in the work, and the result is obtained directly from the generative model. Furthermore, skip connection and batch normalization are integrated into the proposed model. Skip connection transmits the gradient from shallow to deep layers. It has a transitional effect on the shallow and deep features. Batch normalization can effectively avoid gradient vanishing and explosion. Both approaches enhance the processing capability of the model. Result In this study, the entire network model and the single generative model are compared; the two sets of models represent CGAN and CNN methods, respectively. Results show that the entire network model has a better processing effect than the single generative model. This finding proves that the discriminative model improves the effect of the generative model during training. Afterward, eight existing methods are applied for comparison with the proposed method. By subjectively comparing the results of these methods, we find that our method achieves a better effect in terms of brightness, clarity, and color restoration. By using the peak signal-to-noise ratio (PSNR), histogram similarity (HS), and structural similarity (SSIM) as the objectives of comparison, our method exhibits improvements of 0.7 dB, 3.9%, and 8.2%, respectively. Meanwhile, the processing time of each method is compared. By using a graphics processing unit (GPU) for acceleration, the proposed method becomes much faster than the other methods, especially traditional central processing unit (CPU)-based methods. The proposed method can meet the requirement of real-time applications. Furthermore, for several low-illumination images with bright parts, our method does not enhance these parts, whereas other existing methods always over-enhance the bright parts. Conclusion A conditional generative adversarial network-based method for low-illumination image enhancement is proposed. Experimental results show that the method proposed is more effective than existing methods not only in perception but also in speed.

image processing; low-illumination image enhancement; convolutional neural network (CNN); conditional generative adversarial network (CGAN); deep learning