Image Decomposition: Theory, Numerical Schemes, and Performance Evaluation
This work addresses the need for standardized evaluation in image processing, but it is incremental as it focuses on performance assessment rather than introducing new decomposition techniques.
The paper tackles the problem of evaluating image decomposition models that separate structures, textures, and noise using total variation and functional spaces, proposing a method to assess their performance to better understand model behavior.
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.