Adaptive Image Denoising by Targeted Databases

TL;DR

This paper introduces a data-dependent image denoising method that uses targeted databases to improve restoration quality, employing optimal filter design, sparsity minimization, and Bayesian priors for various image types.

Contribution

It presents a novel denoising algorithm that selectively uses relevant patches from targeted databases, with a systematic filter design and localized Bayesian priors.

Findings

Outperforms existing denoising methods in experiments

Effective across text, multiview, and face images

Provides a systematic framework for filter optimization

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 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.