Image patch priors become a popular tool for image denoising. The Gaussian mixture model (GMM) is remarkably effective in modelling natural image patches. However, GMM prior learning using the expectation maximisation (EM) algorithm is sensitive to the initialisation, often leading to low convergence rate of parameter estimation. In this study, a novel sampling method called random neighbourhood resampling (RNR) is proposed to improve the accuracy and efficiency of parameter estimation. An enhanced GMM (EGMM) learning algorithm is further developed by incorporating RNR into the EM algorithm to initialise and update the GMM prior. The learned EGMM prior is applied in the expected patch log-likelihood (EPLL) framework for image denoising. The effectiveness and performance of the proposed RNR and EGMM algorithm are demonstrated via extensive experimental results comparing with the state-of-the-art image denoising methods, the experimental results show the higher PSNR result of the denoised images using the proposed method. Meanwhile, the authors verified that the proposed method can efficiently reduce the time of image denoising compared with the basic EPLL method.

中文翻译：

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基于随机邻域重采样的图像补丁先验学习

图像补丁先验成为图像去噪的流行工具。高斯混合模型（GMM）在建模自然图像补丁方面非常有效。但是，使用期望最大化（EM）算法的GMM先验学习对初始化敏感，通常会导致参数估计的收敛速度较低。在这项研究中，提出了一种称为随机邻域重采样（RNR）的新型采样方法，以提高参数估计的准确性和效率。通过将RNR合并到EM算法中以初始化和更新GMM，可以进一步开发增强型GMM（EGMM）学习算法。将学习到的EGMM先验应用于期望的补丁对数似然（EPLL）框架中以进行图像降噪。通过广泛的实验结果证明了所提出的RNR和EGMM算法的有效性和性能，与最新的图像去噪方法相比，实验结果表明，所提出的方法对去噪图像具有更高的PSNR结果。同时，作者证实，与基本的EPLL方法相比，该方法可以有效减少图像去噪的时间。