Spectral Image

Spectral imaging captures information across multiple wavelengths, aiming to reconstruct detailed spectral data from limited measurements or enhance image quality. Current research focuses on developing efficient deep learning models, including convolutional neural networks (CNNs), transformers, and recurrent neural networks (RNNs), often integrated with techniques like deep unfolding and generative models (e.g., diffusion models) to address challenges such as noise reduction, inpainting, and super-resolution. These advancements are significantly impacting diverse fields, from remote sensing and medical imaging to mobile photography and material analysis, by improving image quality, enabling new diagnostic capabilities, and facilitating more accurate material identification.