Abstract
With the development of intelligent technology, the concept of smart city, which is used to optimize urban management and services and improve the quality of life of citizens, has gradually been integrated into human social life. However, dark lighting environments in reality, such as insufficient light at night, cause or block photographic images in low brightness, severe noise, and a large number of details are lost, resulting in a huge loss of image content and information, which hinders further analysis and use. Such problems not only exist in the development of smart cities, but also exist in traditional criminal investigation, scientific photography and other fields, such as the accuracy of low-light image. However, in the current research results, there is no perfect means to deal with the above problems. Therefore, the study of low-light image enhancement has important theoretical significance and practical application value for the development of smart cities. In order to improve the quality of low-light enhanced images, this paper tries to introduce the luminance attention mechanism to improve the enhancement efficiency. The main contents of this paper are summarized as follows: using the attention mechanism, a method of low-light image enhancement based on the brightness attention mechanism to generative adversarial networks is proposed. This method uses brightness attention mechanism to predict the illumination distribution of low-light image and guides the enhancement network to enhance the image adaptiveness in different luminance regions. At the same time, u-NET network is designed and constructed to improve the modeling process of low-light image. We verified the performance of the algorithm on the synthetic data set and compared it with traditional image enhancement methods (HE, SRIE) and deep learning methods (DSLR). The experimental results show that our proposed network model has relatively good enhancement quality for low-light images, and improves the overall robustness, which has practical significance for solving the problem of low-light image enhancement in smart cities.
Low Light Image Enhancement Network With Attention Mechanism and Retinex Model
