 Two-dimensional singular-value decomposition

Two-dimensional Singular Value Decomposition (2D SVD) is a concept employed mainly in image processing and data analysis, where data is represented as a two-dimensional matrix (e.g., an image represented by pixel intensity values). It is an extension of the traditional singular value decomposition (SVD), which is typically applied to one-dimensional matrices (vectors) or higher-dimensional tensors.