Low Light Image Enhancement in USV Imaging System Via U-Net and Attention Mechanism

Images captured by Unmanned Surface Vessel (USV) have a wide range of applications in various fields, such as maritime object detection, remote sensing, and autonomous transportation. However, cameras often suffer from a low light environment, resulting in low contrast, high noise, and poor quality image, causing identification difficulties and machine decision errors. In recent years, convolutional neural networks have developed rapidly, which have strong generalization ability and can extract different levels of information, especially high-level information. Therefore, to preprocess low light images before advanced computer vision tasks of USV, we proposed a deep learning-based end-to-end convolutional network for low light enhancement in USV imaging system. The advantage of our model is using U-Net as the basic architecture to gain multi-scale feature maps with improvements, including attention mechanism and dense connection. Besides, we pay attention to edge information given images' edge loss. With the unique network structure, our model can effectively increase the brightness and contrast of dark aquatic images. Experiments have been carried out on testing images to analyze our proposed method with several latest imaging methods. The experimental results show its outstanding performance in both subjective and objective evaluation.