Contrast transfer function of de-noising algorithms.

This paper presents a comprehensive study on the contrast transfer function of de-noising algorithms. In order to cover a broad variety of methods, 45 de-noising algorithms are chosen considering their recognized efficiency in the different application domains of image processing. Advanced methods are targeted: wavelet transform-based algorithms with Daubechies, symlets, curvelets, contourlets, patch-based methods such as BM3D, NL-means algorithms and deep learning approaches; in addition, classical spatial filtering methods are considered, such as Wiener, median, Gauss filtering, and adaptive filtering approaches such as anisotropic diffusion and synthetic aperture radar filtering. The contrast transfer function is provided for each algorithm. Ranking of the set of de-noising algorithms is established according to proposed metrics. The paper provides practical methodology and novel results dedicated to the evaluation of the contrast transfer function of de-noising approaches from literature.