Iterative regularization algorithms for image denoising with the TV-Stokes model
This is an incremental improvement for image processing researchers focusing on denoising techniques.
The authors tackled image denoising with Gaussian noise by extending iterative regularization algorithms from the ROF model to the TV-Stokes model, resulting in improved restoration quality over the original method as shown in experiments.
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.