Cross Spectral

Cross-spectral analysis focuses on matching or aligning data from different spectral domains, such as visible light and infrared, or different types of spectrograms. Current research emphasizes developing robust algorithms, often employing deep learning architectures like GANs and specialized neural networks, to overcome challenges posed by data heterogeneity and limited paired training data. These advancements are crucial for applications ranging from image registration and biometrics (e.g., face and periocular recognition) to more general multi-modal data fusion problems, improving accuracy and robustness in various fields.