Applied Mathematics | Signal Processing
This study examines the relatively recent development of a “fixed-point proximity” approach to one type of minimization problem, considers its application to image denoising, and explores convergence and divergence of the iterative algorithm beyond a (previously supplied) theoretically guaranteed convergence bound on one of the parameters (𝜆). While reviewing the fixed-point proximity approach and its application to image denoising, we aim to communicate the concepts and details in a way that will facilitate understanding for undergraduates and for scholars from other subfields. In the latter portion of our study, the numerical experiment provides thought-provoking data on the effects that parameters 𝜇 and 𝜆 have on convergence or divergence once 𝜆 exceeds the range known to guarantee convergence.
Weddle, Daniel and Guo, Jianfeng
"Fixed-Point Proximity Minimization: A Theoretical Review and Numerical Study,"
OUR Journal: ODU Undergraduate Research Journal: Vol. 8
, Article 10.
Available at: https://digitalcommons.odu.edu/ourj/vol8/iss1/10