Iterative regularization algorithms for image denoising with the TV-Stokes model

Autor:	Wu, Bin, Marcinkowski, Leszek, Tai, Xue-Cheng, Rahman, Talal
Rok vydání:	2020
Předmět:	Mathematics - Numerical Analysis Computer Science - Computer Vision and Pattern Recognition Mathematics - Optimization and Control
Druh dokumentu:	Working Paper
Popis:	We propose a set of iterative regularization algorithms for the TV-Stokes model to restore images from noisy images with Gaussian noise. These are some extensions of the iterative regularization algorithm proposed for the classical Rudin-Osher-Fatemi (ROF) model for image reconstruction, a single step model involving a scalar field smoothing, to the TV-Stokes model for image reconstruction, a two steps model involving a vector field smoothing in the first and a scalar field smoothing in the second. The iterative regularization algorithms proposed here are Richardson's iteration like. We have experimental results that show improvement over the original method in the quality of the restored image. Convergence analysis and numerical experiments are presented.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2009.11976 Zobrazit plný text záznamu View this record from Arxiv