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Image denoising technology is one of the forelands in the field of computer graphic and computer vision. Non-local means method is one of the great performing methods which arouse tremendous research. In this paper, an improved weighted non-local means algorithm for image denoising is proposed. The non-local means denoising method replaces each pixel by the weighted average of pixels with the surrounding neighborhoods. The proposed method evaluates on testing images with various levels noise. Experimental results show that the algorithm improves the denoising performance.

In view of the defect that the migration operator randomness is too strong to reduce the bacteria overall fitness, but not conducive to the overall optimization when bacteria foraging algorithm is in the optimization process. Then the pheromone of ant colony algorithm is introduced into the bacteria foraging algorithm. Through the migration of maximum pheromone and smaller pheromone, this paper improves the convergence precision, the global convergence ability of the original algorithm and establishes the hybrid bacteria foraging optimization algorithm. Meanwhile, the Cauchy variation is introduced in reproduction operation in order to enhance the global searching ability of algorithm. Then the paper adopts high dimensional complex standard functions to test these three kinds of bionic algorithms. The results indicate that the new algorithm significantly improves the search speed, partly avoids the local convergence problem and is more suitable for solving optimization problems of complex high dimensional engineering.