MULTI-QUADRATIC DYNAMIC PROGRAMMING PROCEDURE OF EDGE– PRESERVING DENOISING FOR MEDICAL IMAGES
- Institute of Applied Mathematics and Computer Science, Tula State University-Russia
Keywords: Dynamic programming, Bayesian framework, Markov random fields (MRFs), Edges–preserving smoothing, Separable optimization, Potential functions
Abstract. In this paper, we present a computationally efficient technique for edge preserving in medical image smoothing, which is developed on the basis of dynamic programming multi-quadratic procedure. Additionally, we propose a new non-convex type of pair-wise potential functions, allow more flexibility to set a priori preferences, using different penalties for various ranges of differences between the values of adjacent image elements. The procedure of image analysis, based on the new data models, significantly expands the class of applied problems, and can take into account the presence of heterogeneities and discontinuities in the source data, while retaining high computational efficiency of the dynamic programming procedure and Kalman filterinterpolator. Comparative study shows, that our algorithm has high accuracy to speed ratio, especially in the case of high-resolution medical images.