Hybrid-Input Convolutional Neural Network-Based Underwater Image Quality Assessment

Since precisely sensing the underwater environment is a challenging prerequisite for safe and reliable underwater operation, interest in underwater image processing is growing at a rapid pace. In engineering applications, there are redundant underwater images addressed in real-time on the remotely operated vehicle (ROV). It puts the equipment or operators under great pressure. To relieve this pressure by transmitting images selectively according to the degradation degree, we propose an end-to-end hybrid-input convolutional neural network (HI-CNN) to predict the degradation of underwater images. First, we propose a feature extraction module to extract the features of original underwater images and saliency maps concurrently, which is composed of two branches with the same structure and shared parameters. Second, we design an end-to-end model to predict the quality scores of original images, which consists of a feature extraction module and a prediction module. Finally, we establish a real-world dataset to make the proposed model be duplicated in the practical underwater environment. Through several experiments, we demonstrate that the proposed model outperforms existing models in predicting underwater image quality.