Filtro de restauración de imágenes basado en la transformada discreta del coseno y el análisis de componentes principales

An algorithm for restoration of the images contaminated by additive white Gaussian noise is proposed. The groups of patches are found by the proposed block similarity search algorithm of reduced complexity performed on block patches in transform domain. When the noise variance is small, the proposed filter uses an additional stage based on principal component analysis; otherwise the experimental Wiener filtering is performed. The obtained filtering results are compared to the state of the art filters in terms of peak signal-to-noise ratio and structure similarity index.