2010/04/08 by Gaurab Basu, Basu, Gaurab, Kaushik Ray +3
Computer Science · Engineering · Physics and Astronomy · #Data Analysis #FOS: Physical sciences #Image and Signal Denoising Methods #Medical Image Segmentation Techniques #Remote-Sensing Image Classification #Statistics and Probability (physics.data-an) #physics.data-an
paper · pdf · doi:10.48550/arxiv.1004.1356
7 pages, 5 figures
arxiv created 2010/04/08 · openalex publication_date 2010/04/08 · arxiv updated 2010/04/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We make use of recent results from random matrix theory to identify a derived threshold, for isolating noise from image features. The procedure assumes the existence of a set of noisy images, where denoising can be carried out on individual rows or columns independently. The fact that these are guaranteed to be correlated makes the correlation matrix an ideal tool for isolating noise. The random matrix result provides lowest and highest eigenvalues for the Gaussian random noise for which case, the eigenvalue distribution function is analytically known. This provides an ideal threshold for removing Gaussian random noise and thereby separating the universal noisy features from the non-universal components belonging to the specific image under consideration.