Spectral Degradation

Spectral degradation encompasses the loss or distortion of spectral information in various data types, such as images and audio signals, hindering accurate analysis and reconstruction. Current research focuses on developing sophisticated models, including deep unfolding networks and physics-inspired degradation models, to estimate and compensate for these degradations, often employing techniques like adaptive parameter prediction and iterative refinement. This work is crucial for improving the quality of data in diverse applications, ranging from medical imaging and hyperspectral image fusion to video restoration and spatial audio processing. The development of robust and efficient methods for mitigating spectral degradation is essential for advancing these fields.