We propose a data-dependent denoising procedure to restore noisy images. Different from existing denoising algorithms which search for patches from either the noisy image or a generic database, the new algorithm finds patches from a database that contains only relevant patches. We formulate the denoising problem as an optimal filter design problem and make two contributions. First, we determine the basis function of the denoising filter by solving a group sparsity minimization problem. The optimization formulation generalizes existing denoising algorithms and offers systematic analysis of the performance. Improvement methods are proposed to enhance the patch search process. Second, we determine the spectral coefficients of the denoising filter by considering a localized Bayesian prior. The localized prior leverages the similarity of the targeted database, alleviates the intensive Bayesian computation, and links the new method to the classical linear minimum mean squared error estimation. We demonstrate applications of the proposed method in a variety of scenarios, including text images, multiview images and face images. Experimental results show the superiority of the new algorithm over existing methods.

提出了一种数据依赖性去噪过程以还原图像。该算法从仅包含相关补丁的数据库中查找补丁，将去噪问题制定为最优滤波器设计问题，并通过解决组稀疏最小化问题确定去噪滤波器的基函数。通过考虑本地贝叶斯先验确定去噪滤波器的谱系数，将该算法与现有算法相比，在文本图像，多视图图像和面部图像等各种场景下表现出了优越性。

有针对性数据库的自适应图像去噪