Attention-guided lightweight generative adversarial network for low-light image enhancement in maritime video surveillance

Abstract Benefiting from video surveillance systems that provide real-time traffic conditions, automatic vessel detection has become an indispensable part of the maritime surveillance system. However, high-level vision tasks generally rely on high-quality images. Affected by the imaging environment, maritime images taken under poor lighting conditions easily suffer from heavy noise and colour distortion. Such degraded images may interfere with the analysis of maritime video by regulatory agencies, such as vessel detection, recognition and tracking. To improve the accuracy and robustness of detection accuracy, we propose a lightweight generative adversarial network (LGAN) to enhance maritime images under low-light conditions. The LGAN uses an attention mechanism to locally enhance low-light images and prevent overexposure. Both mixed loss functions and local discriminator are then adopted to reduce loss of detail and improve image quality. Meanwhile, to satisfy the demand for real-time enhancement of low-light maritime images, model compression strategy is exploited to enhance images efficiently while reducing the network parameters. Experiments on synthetic and realistic images indicate that the proposed LGAN can effectively enhance low-light images with better preservation of detail and visual quality than other competing methods.