FSCM: A GAN Framework for Infrared Hyperspectral Image Colorization
A new paper on arXiv introduces FSCM, a generative adversarial network (GAN) framework designed to colorize infrared hyperspectral images. Thermal infrared imaging is robust to illumination changes and smoke, aiding all-weather perception, but lacks natural color and fine texture, hindering target recognition and visual interpretation. Existing colorization methods rely on single-band images, causing structural distortion and semantic confusion due to insufficient spectral cues. Infrared hyperspectral images offer rich spectral data, but current single-band frameworks poorly model spatial-spectral coupling and weak textures. FSCM addresses these issues with a spectral-information-guided GAN, featuring a frequency-enhanced spatial-spectral state-space generator built from cascaded FSB units. Each FSB integrates state-space modeling and frequency enhancement to improve colorization quality. The paper is published on arXiv under ID 2605.15880.
Key facts
- FSCM is a GAN framework for infrared hyperspectral image colorization.
- Thermal infrared imaging is robust to illumination variations and smoke interference.
- Existing infrared colorization methods use single-band images, leading to structural distortion.
- Infrared hyperspectral images provide rich spectral responses and material information.
- FSCM uses a spectral-information-guided GAN with a frequency-enhanced generator.
- The generator consists of cascaded FSB units integrating state-space modeling.
- The paper is available on arXiv with ID 2605.15880.
- The approach targets all-weather perception and target recognition.
Entities
Institutions
- arXiv