Spectral Translation

Spectral translation focuses on transforming spectral data between different domains, such as near-infrared (NIR) to RGB, or adapting images across different frequency spectrums to improve image generation or address domain discrepancies in machine learning. Current research emphasizes developing deep generative models, often incorporating multi-scale architectures and leveraging color space representations like HSV, to achieve high-fidelity translations while maintaining computational efficiency. These advancements are improving image generation quality, enabling cross-modal analysis in spectroscopy, and enhancing the robustness of computer vision systems in challenging conditions like low light.