In this work, we present procedures for image denoising based on dynamic programming procedure for maximum a posteriori probability estimation. A new non-convex type regularization is used, with ability to flexibly set a priori preferences, using different penalties for various ranges of differences between the values of adjacent image elements. Proposed procedures can take into account heterogeneities and discontinuities in the source data.
['Pham Cong Thang', 'A. V. Kopylov']
Parametric procedures for image denoising with flexible prior model