A Deep Learning Framework for Semantic Colorization of Synthetic Aperture Radar Images

Key Points

• The aim is to improve the visualization of synthetic aperture radar images using a deep learning framework.
• Developed a deep learning framework for image colorization
• Tested effectiveness on synthetic aperture radar images
• Utilized various neural network architectures for optimal results
• Achieved significant improvements in image clarity and visual appeal
• Demonstrated enhanced semantic understanding of radar images
• Achieved high accuracy in matching original image colors