Image harmonization and de-harmonization based on singular value decomposition (SVD) in medical domain

The proposed SVD-based harmonization and de-harmonization algorithms present a robust solution to the challenges of image variability in medical imaging. By addressing inconsistencies across different datasets and imaging modalities, while preserving crucial diagnostic information, the techniques enhance the visual quality and clinical utility of medical images. The method's strong performance in both homology and heterology experiments demonstrates its broad applicability and potential to improve the effectiveness of machine learning models in various medical imaging tasks.