Research On Image Denoising Algorithm Based On The Minimum Error Rate Bayesian Decision-making And Smoothing Filtering

When the image signals are generated and transmitted, they are regularly interfered with by a variety of noise. In general, real images usually contain noise. Therefore, in image processing work, it is very important that we use appropriate methods to remove noise interference before carrying out image segmentation; pattern recognition. Average filtering and median filtering are commonly used to remove image noise. There is a usual way to remove noise that is, a variety of improved denoising algorithms derive from average filtering and median filtering. However, these methods usually smooth each pixel of image. So they will revise the image's edges and details which should be retained in the image. As a result, the imformation of image is changed. In recent years, wavelet analysis has been rapidly developed. In image processing, wavelet image denoising has been paid more attention to by domestic and foreign scholars and has become hot topic. Then a series of methods appeared, such as adaptive soft (hard) threshold denoising method, modulus maxing denoising method, optimal fuzzy threshold denoising method. They have achieved some results. But it is difficult and controversial to select the effective and rational wavelet or wavelet parameters in the application of wavelet denoising. Therefore, the focus of this paper is how to effectively remove noise and preserve image clarity as much as possible.Firstly, this paper briefly describes the significance of image denoising and the status quo. Then we give an introduction of the traditional image denoising methods. Next, we analyse the basic theory of Bayesian decision-making and general Bayesian decision-making methods. On this basis, we learn that the premise of denoising is accurate detection of noise through observating the image and its histogram. No matter which the denoising method we use, we can not accurately detect each noise point. So we can make error rate as small as possible when we detect denoising, and only smooth the noise point.In this paper, we use the minimum error rate of Bayesian decision-making to detect noise. And different filtering methods are used to remove different type's noises. At last we only use the point which is not noise point to filter. Research work focuses on the following aspects: 1. Introducing the minimum error rate Bayesian decision-making detects the noise. Then it will enable the error rate of detecting noise as small as possible. Because the calculation of the class conditional probability desity is difficult in the minimum error rate Bayesian decision-making, we use discrete method to compute it to avoid inaccurate estimates.2. Introducing, simplifying and improving Otsu method computes threshold. We apply simplified and improved Otsu method to automaticly select the optimal threshold for noise and object. Thus it avoids the possibility of noise gray absolutization and the complex process of selecting threshold. In additon, we can also obtain two thresholds of salt and pepper noise image once.3. Using different smoothing methods removes the different type's noise. Regardless of using filtering method, the first step is to detect the participant pixel in order to prevent secondary pollution. Then only using non-noise points smooth the current point which is judged to be a noise. Thereby it precludes the possibility of secondary pollution which be produced due to using noise pixel to filter.4. Anslysing the image quality after treatment implements loops detection filtering. The condition which judges image quality is Signal-to-Noise. So that we will get the highest signal to noise ratio and optimal image quality by loop processing. The experimental results show that the algorithm is much better than the de-noising effect of some other ways; especially for the high noise density of the image has a better effect.In order to verify the effectiveness of the algorithm, this paper adopts VC++6.0 programming environment to test the image noise with different noise types, noise intensity respectively. The experimental results are compared with the traditional denoising method. The results show that the proposed algorithm can effectively remove the noise and retain image details to some extent.