Adaptive Image Denoising by Targeted Databases
This addresses image denoising for applications such as text, multiview, and face images, but it is incremental as it builds on existing denoising algorithms with targeted databases and optimization formulations.
The paper tackles image denoising by using a targeted database of relevant patches, formulating it as an optimal filter design problem with contributions in basis function determination via group sparsity and spectral coefficients via a localized Bayesian prior. Experimental results demonstrate superiority over existing methods in scenarios like text, multiview, and face images.
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.