Infrared Image Enhancement in Maritime Environment with Convolutional Neural Networks

Image enhancement approach with Convolutional Neural Network (CNN) for infrared (IR) images from maritime environment, is proposed in this paper. The approach includes different CNNs to improve the resolution and to reduce noise artefacts in maritime IR images. The denoising CNN employs a residual architecture which is trained to reduce graininess and fixed pattern noise. The super-resolution CNN employs a similar architecture to learn the mapping from a low-resolution to multi-scale high-resolution images. The performance of the CNNs is evaluated on the IR test dataset with standard evaluation methods and the evaluation results show an overall improvement in the quality of the IR images.