Image transfer based on deep learning methods can achieve good results in face illumination processing. However, data constraints and generalization ability restrict the further development of these methods in this field. In this paper, we propose small data assisting face image illumination normalization. For data constraints, we train the network model on a small number of image pairs. In terms of generalization ability, the proposed normalization network parameters are different for processing different face images. Small data learning can provide prior knowledge, and the reconstruction process can guide detail generation. Therefore, the small data learning network and the reconstruction network are complementary to each other in image generating mode when only a small quantity of data is available. We use this network mode to normalize the illumination and reconstruct the super-resolution of face images. After illumination normalization, super-resolution reconstruction can obtain more precise face information and further improve the face recognition rate. Experiments show that the proposed method has good normalization performance when only 500 face image pairs are used for training.

基于深度学习方法的图像传输可以在面部照明处理中取得良好的效果。但是，数据约束和泛化能力限制了这些方法在该领域的进一步发展。在本文中，我们提出了有助于人脸图像照度归一化的小数据。对于数据约束，我们在少量图像对上训练网络模型。就泛化能力而言，所提出的归一化网络参数对于处理不同的面部图像而言是不同的。小数据学习可以提供先验知识，而重构过程可以指导细节生成。因此，当仅少量数据可用时，小数据学习网络和重建网络在图像生成模式中彼此互补。我们使用这种网络模式来标准化照明并重建人脸图像的超分辨率。经过照度归一化后，超分辨率重建可以获得更精确的人脸信息，从而进一步提高人脸识别率。实验表明，该方法在仅使用500对人脸图像进行训练时，具有良好的归一化性能。