Towards Low-Visibility Enhancement in Maritime Video Surveillance: An Efficient and Effective Multi-Deep Neural Network

Abstract

Limited by insufficient illumination, the images collected by maritime imaging devices often suffer from low brightness, low contrast, low signal-to-noise ratio, severe information loss, and so on. The above problems restrict the development of maritime- related work such as intelligent supervision, collision warning, accident investigation, etc. To improve the imaging quality of maritime video images, we propose an efficient and effective multi-deep neural network (termed EEMNN) for low-visibility enhancement. In particular, we fuse the multi-scale information extracted from the encoder-decoder module using the dense blocks (DBs) and attention blocks (ABs). It is capable of enhancing the fused information leading to preserving the edges, textures, and other fine details. To prevent the overexposure of enhanced images, we fuse and reconstruct the output features of DBs and ABs with the raw low-light image to get the final enhanced image through two residual blocks (RBs). The mixing of multiple network modules can effectively improve the generalization ability and robustness of our network. Through extensive experiments, EEMNN has higher objective evaluation indicators, more efficient enhancement, more natural maritime scenes, and stronger detail-preservation capabilities compared with other enhancement methods.