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

TL;DR

This paper introduces iterative regularization algorithms tailored for the TV-Stokes model to enhance image denoising from Gaussian noise, extending classical methods with a two-step process involving vector and scalar field smoothing.

Contribution

It develops novel iterative regularization algorithms specifically designed for the TV-Stokes model, improving image restoration quality over existing methods.

Findings

Improved image quality in denoising results.

Convergence of the proposed algorithms is established.

Numerical experiments validate the effectiveness of the methods.

Abstract

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.