Based on the problems that traditional image de-noising algorithms easily to lose details features and always has low signal-to-noise ratio, inspired by the cross breeding method and the genetic algorithm, this paper proposes an image hybrid wavelet transform image de-noising algorithm based on Bayesian estimation. The proposed algorithm uses the Bayesian wavelet de-noised image as the male parent, and the Wiener filtering image as the female parent, the picked individual was prepared for the crossover and mutation operations. The optimal offspring will be choosing as the final solution algorithm and the decoding reduction as the de-noised image. The peak signal to noise ratio of the algorithm is much higher than that of the traditional algorithm, and the visual effect is better. The experimental results show that this method can not only eliminate the image noise effectively, but also can preserve the image edges and other details.

Abstract. The paper first analyzed the property of sample confidence measure function applied by noise reduction algorithm, explained the reason of this function being not suitable for multi-class problems. Then a more targeted confidence measure function was designed, and based on this function, an enhanced de-noise algorithm of ensemble parameters learning was proposed. Thus the discriminative learning algorithm not only effectively restrain the noise, but also avoid the overfitting of the classifier. Finally, the experimental results and statistical analysis for hypothesis testing verified that the current ensemble parameters learning algorithms of Bayesian network was improved obviously in the performance.