Adaptive Image Denoising by Targeted Databases
Abstract— Adaptive Image Denoising by Targeted Databases. 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 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. < Final Year Projects 2016 >
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.
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