图像小波系数先验模型在图像处理中得到广泛的应用。已有小波系数的建模方法在模型选择、模型参数估计和非高斯噪声图像恢复等方面存在一定限制。利用全贝叶斯神经网络(FBNN)模型对图像小波系数的统计特性进行建模,利用现代粒子采样技术进行估计获得该模型的参数。对单尺度和父子尺度小波系数先验模型的仿真实验表明,基于全贝叶斯神经网络的小波先验模型建模准确,较好地描述了小波系数统计特性,把由此方法获得的单尺度和父子尺度小波系数先验粒子应用于图像去噪处理,仿真结果证实去噪处理后的图像质量在客观指标和主观视觉上都有显著的提高。

Wavelet coefficients prior statistical models of image have been studied widely in the Bayesian-based image processing scopes. In this paper,we derive a precise prior statistical model based on full Bayesian neural network (FBNN). The parameters of the model can be estimated empirically from a sample image set by modern particle samplers (Montel Carlo) methods. The simulated results based on the prior models of single scale and parent-children scale show the model makes it possible to exploit the dependency between the scales. Furthermore,a novel image denoising method based on scale prior particles sampled from the fitted the single scale and parent-children prior models produces the high quality visual effects and peak signal-to-noise ratio (PSNR).