Maritime Infrared Image Super-Resolution Using Cascaded Residual Network and Novel Evaluation Metric

Abstract

Infrared (IR) cameras have been important surveillance sensors for autonomous surface vessels; however, their detection ranges are limited by low resolution. In this study, we collect maritime IR images, analyze the characteristics of those images, and develop datasets for training and testing. Then, a new maritime IR image super-resolution network, maritime infrared super-resolution using cascaded residual network, is developed to reconstruct IR images using a scale of 4. Moreover, different loss functions have different effects on output images; a loss function is set to be a combination of three loss functions, including mean absolute error, mean squared error, and perceptual loss. Peak signal-To-noise ratio and structural similarity index measure cannot effectively describe super-resolution performance. As the novel evaluation metric, Canny edge detection method is used because edges are important for human and target detection algorithms. Finally, experiments are conducted and the results demonstrate that the developed residual network can achieve high-quality reconstructed maritime IR images.