This paper describes the many image decomposition models that allow to separate structures and textures or structures, textures, and noise. These models combined a total variation approach with different adapted functional spaces such as Besov or Contourlet spaces or a special oscillating function space based on the work of Yves Meyer. We propose a method to evaluate the performance of such algorithms to enhance understanding of the behavior of these models.

翻译：本文阐述了多种图像分解模型，这些模型能够实现结构与纹理的分离，或结构、纹理与噪声的分离。这些模型将全变分方法与多种适配的函数空间相结合，例如Besov空间、Contourlet空间，或基于Yves Meyer工作构建的特殊振荡函数空间。我们提出了一种评估此类算法性能的方法，以增进对这些模型行为的理解。