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.

The knowledge graph plays an important role in the construction of artificial intelligence applications. However, the incompleteness of the knowledge graph seriously affects the performance of downstream applications. The problem has fueled a lot of researches on knowledge graph completion (also known as the tasks of link prediction). Reinforcement learning-based multi-hop reasoning that formulates link prediction as a sequential decision problem has also become an interesting and promising approach. Nevertheless, in an incomplete knowledge graph environment, the policy-based agent might travel a large number of low-quality or spurious search trajectories, which inhibits the model performance. Therefore, in this paper, we propose a path and structural features-enhanced reinforcement learning model (referred as PGATRL). First, we leverage the path constraint resource allocation algorithm to mine high-quality inference paths, which can be employed to pre-train the LSTM path encoder module in the reinforcement learning architecture, and thus play a role in guiding the agent’s action decision-making. Second, we exploit the adapted graph attention networks to encode the local structural features of an entity, which can provide more evidence for the agent to find a more suitable reasoning path. With extensive experiments on several benchmark datasets, our proposed approach gains significant improvements compared with the state-of-the-art baselines.