Low-light image enhancement base on brightness attention mechanism generative adversarial networks

With the development of the field of deep learning, image recognition, enhancement and other technologies have been widely used.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 traditional deep learning field, but also exist in 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, we proposed a method of low-light image enhancement based on the brightness attention mechanism and generative adversarial networks . 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.