关键词: 图像去噪, 字典学习, 稀疏表示, 奇异值分解, 贝叶斯估计

Abstract: Overcomplete dictionary was used to represent an image sparsely in order to improve image denoising performance. The sparse representation may represent efficiently the singular geometry of the images with the redundancy of over-complete dictionary. Global image prior model based on the sparse representation of image patches was presented in Bayesian framework. Then maximum a posteriori probability estimator for denoising image was constructed. The dictionary was composed of two orthonormal bases. A method based on singular value decomposition was used for dictionary learning. The orthonormal property was used to update the one chosen basis effectively. The method can improve the performance of image denoising. The experimental results verify the validity of the method.

Key words: image denoising, dictionary learning, sparse representation, Singular Value Decomposition (SVD), Bayesian estimation