Adaptive Image Denoising by Targeted Databases

Abstract

Abstract-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 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.

Real Image Denoising With Feature Attention

“…Generally, denoising algorithms can be categorized as model-based and learning-based. Model-based algorithms include non-local self-similarity (NSS) [18,13,20], sparsity [30,48], gradient methods [46,56,54], Markov random field models [52], and external denoising priors [9,61,42]. The model-based algorithms are computationally expensive, time-consuming, unable to suppress the spatially variant noise directly and characterize complex image textures.…”

Model-Based Design Methodology for Blind Image Restoration

“…In other words, it minimizes the overall mean square error in the process of inverse filtering and noise smoothing [8][9]. The Wiener filtering is a linear estimation of the original image.…”

Patch-based denoising method using low-rank technique and targeted database for optical coherence tomography image

“…And we can solve the above equation by SVD, which is demonstrated by Ref. 18. With SVD, P j can be represented as follows:…”

“…External denoising has the ability to overcome the shortcoming and break the performance bounds. Luo et al 18 proposed a data-dependent denoising procedure to restore noisy image and leverages the similarity of the external database, showing the superiority of the new algorithm over existing methods. A learning-based approach using a neural network, combining denoising results from an internal method and an external method, was proposed by Burger et al 19 Low-rank technique, 20 which is formulated as a minimization problem where the cost function measures the fit between a given matrix (the original data) and an approximating matrix (the optimization data), is subject to a constraint that the approximating matrix has reduced rank.…”