Low-Light Image Enhancement for UAVs With Multi-Feature Fusion Deep Neural Networks

Object Detection in low-light aerial images is a challenging problem due to considerable variation in brightness and varying contrast. Deep Learning-based approaches have recently demonstrated great promise in image enhancement. Many existing neural networks used for image quality enhancement first encode the input into low-resolution representations and then decode these representations back to a higher resolution for the contextual information. However, this method leads to the loss of semantic content. Recent research has demonstrated the advantage of maintaining high-resolution information along with lower resolution representations, which maintains image features throughout the network. In this paper, we propose a novel architecture named RNet for low-light image enhancement of aerial images. The proposed network contains multi-resolution branches for better understanding of different levels of local and global context through different streams. The performance of RNet is evaluated on a recent synthetic dataset. We also present a comprehensive evaluation with a representative set of state-of-the-art enhancement techniques and neural net architectures.