论文标题
电视模型的迭代正则化算法用于图像降级
Iterative regularization algorithms for image denoising with the TV-Stokes model
论文作者
论文摘要
我们为TV-Stokes模型提出了一组迭代正则化算法，以恢复具有高斯噪声的嘈杂图像的图像。这些是针对图像重建的经典鲁丁式 - XHOS-FATEMI（ROF）模型提出的迭代正则化算法的一些扩展，该模型是一个涉及标量场平滑的单步模型，用于图像重建的电视 - stokes模型，用于图像重建模型，这是两个步骤模型，该模型涉及第一个和callecor calcalar Field Compliperstigt的两个步骤模型。这里提出的迭代正则化算法是理查森的迭代。我们的实验结果表明，在恢复图像的质量方面，对原始方法的改善。提出了收敛分析和数值实验。
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.
