Abstract
Low-rank approximation of matrices plays an important role in many application scenarios, including image denoising. This paper introduces a new low-rank approximation method named minimum unbiased risk estimate formulation of 2DPCA (MURE-2DPCA). MURE-2DPCA starts by considering the problem of estimating noise-free matrices from observations, and can exhibit robustness to outliers. In the case of a single data matrix constructed with Gaussian vectors, we find that the optimal dimension of the principal subspace can be determined automatically from the data itself. Based on MURE-2DPCA, a three-step algorithm is developed for color image denoising. Experiments demonstrate the ability and efficiency of our algorithm in achieving the denoising task.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
