A robust separable image denoising based on relative intersection of confidence intervals rule

Many microscopy images, or 3D depth maps can be represented using piecewise constant models. They usually contain noise due to sensor imperfectness. In this paper, an improved separable denoising method based on the relative intersection of confidence intervals rule is proposed. The method uses median averaging and is robust to outliers and different noise distributions. It over-performs competitive methods in the sense of edge preservation.