SVD-based image filtering improvement by means of image rotation

This article shows that image filtering based on SVD favors the denoising in the line (horizontal) and column (vertical) direction since the matrix SVD is equivalent to a simultaneous line and column vector principal component analysis (PCA). It also proposes a simple algorithm based on PCA-filtering processed on a rotated image such that the principal directions becomes vertical or horizontal. It brings an important improvement in the final image quality since the filtering in every image direction is improved.