基于广义高斯混合模型的图像去噪算法研究

Image Denoising Algorithm Based on Generalized Gaussian Mixture Model

摘要: 图像在采集和传输过程中常常会受到噪声的干扰，这会严重影响图像的质量。因此，高效的图像去噪方法成为图像处理领域的重要研究课题。对图像噪声进行精准建模是提升图像去噪性能的关键步骤，本文针对这一问题，提出了一种基于广义高斯混合模型的低秩矩阵分解方法。通过假设噪声服从广义高斯混合分布，精确地刻画复杂的噪声特性，并通过低秩矩阵分解捕获数据的主要结构特征。为优化模型参数，采用期望最大化算法进行迭代更新。在合成数据和真实图像数据集上的实验表明，该模型优于本文其他对比模型，表明该算法在图像去噪方面有一定的优势。

Abstract: Images are often disturbed by noise during acquisition and transmission, which can seriously affect the quality of images. Therefore, efficient image denoising methods have become an important research topic in the field of image processing. Accurate modeling of image noise is a key step to improve the performance of image denoising, and this paper proposes a low-rank matrix decomposition method based on the generalized Gaussian mixture model to address this problem. By assuming that the noise obeys a generalized Gaussian mixture distribution, the complex noise characteristics are accurately portrayed, and the main structural features of the data are captured by the low-rank matrix decomposition. To optimize the model parameters, an expectation maximization algorithm is used for iterative updating. Experiments on synthetic data and real image datasets show that the model outperforms other comparative models in this paper, indicating that the algorithm has some advantages in image denoising.