Color Transfer Pulse-Coupled Neural Networks for Underwater Robotic Visual Systems

Abstract

With rapid developments in cloud computing, artificial intelligence, and robotic systems, ever more complex tasks, such as space and ocean exploration, are being implemented by intelligent robots. Here, we propose an underwater image enhancement scheme for robotic visual systems. The proposed algorithm and its implementation enhances and outputs an image captured by an underwater robot in real time. In this scheme, pulse-coupled neural network (PCNN)-based image enhancement and color transfer algorithms are combined to enhance the underwater image. To avoid color imbalance in the underwater image and enhance details while suppressing noise, color correction is first carried out on the underwater image before converting it into the hue–saturation–intensity domain and enhancing it by PCNN. The enhanced result improves the color and contrast of the source image and enhances the details and edges of darker regions. Experiments are performed on real world data to demonstrate the effectiveness of the proposed scheme.