Image Processing Procedures Based on Multi-Quadratic Dynamic Programming

Pham Cong Thang


This paper summarizes the doctoral dissertation [1] of the author. The main subject of this thesis is the study and development of a method for edge preserving in image smoothing, which is developed based on multi-quadratic dynamic programming procedure for maximum a posteriori probability estimation. Additionally, a new non-convex type regularization is proposed, with ability to flexibly set a priori preferences, using different penalties for various ranges of differences between the values of adjacent image elements. Procedures of image processing, as presented here, consider heterogeneities and discontinuities in the source data, while retaining high computational efficiency of the dynamic programming procedure and Kalman filter-interpolator. Comparative study shows, that proposed algorithms has high accuracy to speed ratio, especially in the case of high-resolution images.

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Pham Cong Thang (2016). Parametric Image Processing Procedures Based on Multi-Quadratic Dynamic Programming. Ph.D. dissertation, Tula State University, Russia, 140 pages.

Mottl V., et al. (1998). Optimization techniques on pixel neighborhood graphs for image processing. Graph-Based Representations in Pattern Recognition. Computing, Supplement 12. Springer–Verlag/Wien, pp. 135-145.

Nikolova M., Michael K., and Tam C.P. (2010). Fast Nonconvex Nonsmooth Minimization Methods for Image Restoration and Reconstruction. IEEE Transactions on Image Processing, Vol. 19 (12), pp. 3073-3088.

Pham C. T. and Kopylov A. V. (2015). Multi-Quadratic Dynamic Programming Procedure of Edge–Preserving Denoising for Medical Images. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-5/W6, рр. 101-06.

Kopylov A., et al. (2010). A Signal Processing Algorithm Based on Parametric Dynamic Programming. Lecture Notes in Computer Science, Vol. 6134, pp. 280-86.

Kopylov A.V. (2005). Parametric dynamic programming procedures for edge preserving in smoothing of signals and images. Pattern recognition and image analysis, Vol. 15, pp. 227-229.

Dvoenko S. D. (2009). Clustering Sets Based on Distances and Proximities between Its Elements. Sib. Zh. Ind. Mat., Vol. 12 (1), pp. 61–73.

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