A novel RPCA method using log-weighted nuclear and L2,1 norms combined with contrast-limited adaptive histogram equalization (CLAHE) for high dimensional natural and medical image data

Likassa, Habte TadesseChen, Ding-Geng (Din)Sun, Dayu2025-03-142025-03-142024-11-26Likassa, H.T., Chen, D.G., Sun, D. 2024, 'A novel RPCA method using log-weighted nuclear and L2,1 norms combined with contrast-limited adaptive histogram equalization (CLAHE) for high dimensional natural and medical image data', International Journal of Statistics in Medical Research, vol. 13, pp. 275-290. https://DOI.org/10.6000/1929-6029.2024.13.25.1929-602910.6000/1929-6029.2024.13.25http://hdl.handle.net/2263/101507Please read abstract in the article.en© 2024 Likassa et al. This is an open-access article licensed under the terms of the Creative Commons Attribution License.Low-rankComputational statisticsMedical dataRobust principal component analysis (RPCA)Singular value decomposition (SVD)Log weighted nuclear norm (LWNN)Alternating direction method of multipliers (ADMM)State-of-the-art method (SOTA)Contrast limited AHE (CLAHE)Adaptive histogram equalization (AHE)SDG-09: Industry, innovation and infrastructureA novel RPCA method using log-weighted nuclear and L2,1 norms combined with contrast-limited adaptive histogram equalization (CLAHE) for high dimensional natural and medical image dataArticle