Abstract

?-(GAMMA)-CONVERGENCE ANALYSIS AND VARIATIONAL APPROXIMATION ALGORITHM FOR NOISE MINIMIZATION DURING IMAGE PROCESSING

D. I. Lanlege, A.I. Ma?ali and K.Bitrus

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This paper present a Variational Approximation Algorithm for Noise Minimization During Image Processing. Which simplifies the derivative of the algorithm and eliminate blur and minimize Noise. A unified Algorithm has been tested on a set of real image corrupted with different types of noise? The result is compared with those of other standard noise removal algorithms based on some standard performance measure. Furthermore, in line with the bounded variation method in the regularization of inverse problems in image processing. Also, this paper presented a numerical algorithm for further minimization of vibration/blurs in two-dimensional digital image processing for enhanced pattern recognition was presented. An analytical proof of convergence of this algorithm, using the concept of ?- convergence is presented. Keywords: Variation Approximation Algorithm, ?-gamma Convergence, Minimization Noise, Image Processing.

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